Recommendation Engine Guide

<h2 id="recommendation-engine-guide" class="position-relative d-flex align-items-center group"> Recommendation Engine Guide <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="recommendation-engine-guide" aria-haspopup="dialog" aria-label="Share link: Recommendation Engine Guide"> Share link </button> </h2><div id="headingShareModal" class="heading-share-modal" role="dialog" aria-modal="true" aria-labelledby="headingShareTitle" hidden> <div class="hsm-dialog" role="document"> <div class="hsm-header"> <h2 id="headingShareTitle" class="h6 mb-0 fw-bold">Share this section</h2> <button type="button" class="hsm-close" aria-label="Close"> </button> </div> <div class="hsm-body"> <label for="headingShareInput" class="form-label small text-muted mb-1 text-uppercase fw-bold" style="font-size: 0.7rem; letter-spacing: 0.5px;">Permalink</label> <div class="input-group mb-4 hsm-url-group"> <input id="headingShareInput" type="text" class="form-control font-monospace" readonly aria-readonly="true" style="font-size: 0.85rem;" /> <button class="btn btn-primary hsm-copy" type="button" aria-label="Copy" title="Copy"> </button> </div> <div class="small fw-bold mb-2 text-muted text-uppercase" style="font-size: 0.7rem; 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'fa-duotone fa-clipboard-check':'fa-duotone fa-circle-exclamation'; setTimeout(()=>{ icon.className=prev; },1800); } function handleShareClick(e){ e.preventDefault(); const btn=e.currentTarget; const id=btn.getAttribute('data-share-target'); if(id) openModal(id); } function bindShareButtons(){ document.querySelectorAll('.h-share').forEach(btn=>{ if(!btn.dataset.hShareBound){ btn.addEventListener('click', handleShareClick); btn.dataset.hShareBound='1'; } }); } bindShareButtons(); if(document.readyState==='loading'){ document.addEventListener('DOMContentLoaded', bindShareButtons); } else { requestAnimationFrame(bindShareButtons); } document.addEventListener('click', function(e){ const shareBtn=e.target.closest && e.target.closest('.h-share'); if(shareBtn && !shareBtn.dataset.hShareBound){ handleShareClick.call(shareBtn, e); } }, true); document.addEventListener('click', e=>{ if(e.target===modal) closeModal(); if(e.target.closest && e.target.closest('.hsm-close')){ e.preventDefault(); closeModal(); } if(copyBtn && (e.target===copyBtn || (e.target.closest && e.target.closest('.hsm-copy')))) { e.preventDefault(); copyCurrent(); } }); document.addEventListener('keydown', e=>{ if(e.key==='Escape' && isOpen()) closeModal(); }); function trapFocus(){ if(trapBound) return; trapBound=true; modal.addEventListener('keydown', f=>{ if(f.key==='Tab' && isOpen()){ const focusable=[...modal.querySelectorAll('a[href],button,input,textarea,select,[tabindex]:not([tabindex="-1"])')].filter(el=>!el.hasAttribute('disabled')); if(!focusable.length) return; const first=focusable[0]; const last=focusable[focusable.length-1]; if(f.shiftKey && document.activeElement===first){ f.preventDefault(); last.focus(); } else if(!f.shiftKey && document.activeElement===last){ f.preventDefault(); first.focus(); } } }); } if(closeBtn) closeBtn.addEventListener('click', e=>{ e.preventDefault(); closeModal(); }); })(); </script>This guide demonstrates how to build a powerful recommendation engine using Geode. You’ll learn collaborative filtering, content-based recommendations, and graph-based approaches that leverage Geode’s native graph traversal capabilities. <h3 id="overview" class="position-relative d-flex align-items-center group"> Overview <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="overview" aria-haspopup="dialog" aria-label="Share link: Overview"> Share link </button> </h3>Recommendation engines help users discover relevant content, products, or connections. Graph databases excel at recommendations because they naturally model the relationships between users, items, and attributes. Why Geode for Recommendations? <ul> <li>Native graph traversal - Efficiently follow relationship chains</li> <li>Pattern matching - Find users with similar behavior patterns</li> <li>Real-time computation - Generate recommendations on demand</li> <li>Flexible modeling - Easily add new item types and relationships</li> </ul> <h3 id="data-model" class="position-relative d-flex align-items-center group"> Data Model <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="data-model" aria-haspopup="dialog" aria-label="Share link: Data Model"> Share link </button> </h3> <h4 id="core-entities" class="position-relative d-flex align-items-center group"> Core Entities <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="core-entities" aria-haspopup="dialog" aria-label="Share link: Core Entities"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Users who receive recommendations (:User { id: STRING, username: STRING, email: STRING, preferences: MAP, // User preferences created_at: TIMESTAMP, last_active: TIMESTAMP }) // Items being recommended (products, content, etc.) (:Item { id: STRING, name: STRING, description: STRING, category: STRING, price: FLOAT, attributes: MAP, // Flexible attributes popularity_score: FLOAT, created_at: TIMESTAMP }) // Categories for content organization (:Category { id: STRING, name: STRING, parent_id: STRING }) // Tags for fine-grained classification (:Tag { name: STRING, usage_count: INTEGER }) // Features for content-based filtering (:Feature { name: STRING, type: STRING, // "genre", "color", "size", etc. value: STRING }) </code></pre></div> <h4 id="interaction-relationships" class="position-relative d-flex align-items-center group"> Interaction Relationships <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="interaction-relationships" aria-haspopup="dialog" aria-label="Share link: Interaction Relationships"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// User-Item interactions (:User)-[:VIEWED { timestamp: TIMESTAMP, duration_seconds: INTEGER, source: STRING // "search", "recommendation", "browse" }]->(:Item) (:User)-[:PURCHASED { timestamp: TIMESTAMP, quantity: INTEGER, price: FLOAT }]->(:Item) (:User)-[:RATED { rating: FLOAT, // 1-5 scale timestamp: TIMESTAMP, review: STRING }]->(:Item) (:User)-[:WISHLISTED { timestamp: TIMESTAMP }]->(:Item) (:User)-[:CART_ADDED { timestamp: TIMESTAMP, quantity: INTEGER }]->(:Item) // Item relationships (:Item)-[:IN_CATEGORY]->(:Category) (:Item)-[:HAS_TAG]->(:Tag) (:Item)-[:HAS_FEATURE]->(:Feature) (:Item)-[:SIMILAR_TO {score: FLOAT}]->(:Item) (:Item)-[:FREQUENTLY_BOUGHT_WITH {count: INTEGER}]->(:Item) </code></pre></div> <h4 id="schema-setup" class="position-relative d-flex align-items-center group"> Schema Setup <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="schema-setup" aria-haspopup="dialog" aria-label="Share link: Schema Setup"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Constraints CREATE CONSTRAINT user_id_unique ON :User(id) ASSERT UNIQUE CREATE CONSTRAINT item_id_unique ON :Item(id) ASSERT UNIQUE CREATE CONSTRAINT category_id_unique ON :Category(id) ASSERT UNIQUE CREATE CONSTRAINT tag_name_unique ON :Tag(name) ASSERT UNIQUE // Indexes for recommendation queries CREATE INDEX user_id ON :User(id) CREATE INDEX item_id ON :Item(id) CREATE INDEX item_category ON :Item(category) CREATE INDEX item_popularity ON :Item(popularity_score) CREATE INDEX tag_name ON :Tag(name) CREATE INDEX feature_name_value ON :Feature(name, value) </code></pre></div> <h3 id="collaborative-filtering" class="position-relative d-flex align-items-center group"> Collaborative Filtering <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="collaborative-filtering" aria-haspopup="dialog" aria-label="Share link: Collaborative Filtering"> Share link </button> </h3>Collaborative filtering recommends items based on the behavior of similar users. <h4 id="user-based-collaborative-filtering" class="position-relative d-flex align-items-center group"> User-Based Collaborative Filtering <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="user-based-collaborative-filtering" aria-haspopup="dialog" aria-label="Share link: User-Based Collaborative Filtering"> Share link </button> </h4>Find users with similar tastes and recommend what they liked. <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Find users who purchased the same items as me MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) WHERE me <> similar WITH similar, count(DISTINCT item) AS shared_items ORDER BY shared_items DESC LIMIT 50 // Find items they purchased that I haven't MATCH (similar)-[:PURCHASED]->(recommendation:Item) WHERE NOT (me)-[:PURCHASED]->(recommendation) AND NOT (me)-[:VIEWED]->(recommendation) // Optionally exclude viewed items WITH recommendation, count(DISTINCT similar) AS recommender_count, sum(shared_items) AS score ORDER BY score DESC LIMIT $limit RETURN recommendation.id AS id, recommendation.name AS name, recommendation.category AS category, recommendation.price AS price, recommender_count AS similar_users, score </code></pre></div><div class="docs-tabs mb-4"> <ul class="nav nav-tabs" id="tabs-1774880884273666828" role="tablist"><li class="nav-item" role="presentation"> <button class="nav-link active" id="tabs-1774880884273666828-tab-0-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880884273666828-tab-0" type="button" role="tab" aria-controls="tabs-1774880884273666828-tab-0" aria-selected="true" > Go </button> </li><li class="nav-item" role="presentation"> <button class="nav-link" id="tabs-1774880884273666828-tab-1-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880884273666828-tab-1" type="button" role="tab" aria-controls="tabs-1774880884273666828-tab-1" aria-selected="false" > Python </button> </li><li class="nav-item" role="presentation"> <button class="nav-link" id="tabs-1774880884273666828-tab-2-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880884273666828-tab-2" type="button" role="tab" aria-controls="tabs-1774880884273666828-tab-2" 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import ( "context" "database/sql" "log" _ "geodedb.com/geode" ) type Recommendation struct { ID string Name string Category string Price float64 SimilarUsers int Score float64 } func GetCollaborativeRecommendations(ctx context.Context, db *sql.DB, userID string, limit int) ([]Recommendation, error) { rows, err := db.QueryContext(ctx, ` MATCH (me:User {id: ?})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) WHERE me <> similar WITH similar, count(DISTINCT item) AS shared_items ORDER BY shared_items DESC LIMIT 50 MATCH (similar)-[:PURCHASED]->(recommendation:Item) MATCH (me:User {id: ?}) WHERE NOT (me)-[:PURCHASED]->(recommendation) WITH recommendation, count(DISTINCT similar) AS recommender_count, sum(shared_items) AS score ORDER BY score DESC LIMIT ? RETURN recommendation.id AS id, recommendation.name AS name, recommendation.category AS category, recommendation.price AS price, recommender_count AS similar_users, score `, userID, userID, limit) if err != nil { return nil, err } defer rows.Close() var recommendations []Recommendation for rows.Next() { var r Recommendation err := rows.Scan(&r.ID, &r.Name, &r.Category, &r.Price, &r.SimilarUsers, &r.Score) if err != nil { return nil, err } recommendations = append(recommendations, r) } return recommendations, nil } func main() { db, err := sql.Open("geode", "localhost:3141") if err != nil { log.Fatal(err) } defer db.Close() ctx := context.Background() recs, err := GetCollaborativeRecommendations(ctx, db, "user-123", 10) if err != nil { log.Fatal(err) } for _, r := range recs { log.Printf("Recommended: %s (score: %.2f)", r.Name, r.Score) } } </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880884273666828-tab-1" role="tabpanel" aria-labelledby="tabs-1774880884273666828-tab-1-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">import asyncio from dataclasses import dataclass from typing import List from geode_client import Client @dataclass class Recommendation: id: str name: str category: str price: float similar_users: int score: float async def get_collaborative_recommendations( client, user_id: str, limit: int = 10 ) -> List[Recommendation]: """Get recommendations using user-based collaborative filtering.""" async with client.connection() as conn: result, _ = await conn.query(""" MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) WHERE me <> similar WITH me, similar, count(DISTINCT item) AS shared_items ORDER BY shared_items DESC LIMIT 50 MATCH (similar)-[:PURCHASED]->(recommendation:Item) WHERE NOT (me)-[:PURCHASED]->(recommendation) WITH recommendation, count(DISTINCT similar) AS recommender_count, sum(shared_items) AS score ORDER BY score DESC LIMIT $limit RETURN recommendation.id AS id, recommendation.name AS name, recommendation.category AS category, recommendation.price AS price, recommender_count AS similar_users, score """, {"user_id": user_id, "limit": limit}) return [ Recommendation( id=row['id'].as_string, name=row['name'].as_string, category=row['category'].as_string, price=row['price'].as_float, similar_users=row['similar_users'].as_int, score=row['score'].as_float ) for row in result.rows ] async def main(): client = Client(host="localhost", port=3141, skip_verify=True) recs = await get_collaborative_recommendations(client, "user-123", limit=10) for r in recs: print(f"Recommended: {r.name} (score: {r.score:.2f})") asyncio.run(main()) </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880884273666828-tab-2" role="tabpanel" aria-labelledby="tabs-1774880884273666828-tab-2-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-rust" data-lang="rust">use geode_client::{Client, Value}; use std::collections::HashMap; #[derive(Debug)] struct Recommendation { id: String, name: String, category: String, price: f64, similar_users: i64, score: f64, } async fn get_collaborative_recommendations( conn: &mut geode_client::Connection, user_id: &str, limit: i64, ) -> Result<Vec<Recommendation>, Box<dyn std::error::Error>> { let mut params = HashMap::new(); params.insert("user_id".to_string(), Value::string(user_id)); params.insert("limit".to_string(), Value::int(limit)); let (page, _) = conn.query_with_params(r#" MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) WHERE me <> similar WITH me, similar, count(DISTINCT item) AS shared_items ORDER BY shared_items DESC LIMIT 50 MATCH (similar)-[:PURCHASED]->(recommendation:Item) WHERE NOT (me)-[:PURCHASED]->(recommendation) WITH recommendation, count(DISTINCT similar) AS recommender_count, sum(shared_items) AS score ORDER BY score DESC LIMIT $limit RETURN recommendation.id AS id, recommendation.name AS name, recommendation.category AS category, recommendation.price AS price, recommender_count AS similar_users, score "#, &params).await?; let mut recommendations = Vec::new(); for row in &page.rows { recommendations.push(Recommendation { id: row.get("id").unwrap().as_string()?, name: row.get("name").unwrap().as_string()?, category: row.get("category").unwrap().as_string()?, price: row.get("price").unwrap().as_float()?, similar_users: row.get("similar_users").unwrap().as_int()?, score: row.get("score").unwrap().as_float()?, }); } Ok(recommendations) } #[tokio::main] async fn main() -> Result<(), Box<dyn std::error::Error>> { let client = Client::new("127.0.0.1", 3141).skip_verify(true); let mut conn = client.connect().await?; let recs = get_collaborative_recommendations(&mut conn, "user-123", 10).await?; for r in recs { println!("Recommended: {} (score: {:.2})", r.name, r.score); } Ok(()) } </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880884273666828-tab-3" role="tabpanel" aria-labelledby="tabs-1774880884273666828-tab-3-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-typescript" data-lang="typescript">import { createClient, Client } from '@geodedb/client'; interface Recommendation { id: string; name: string; category: string; price: number; similarUsers: number; score: number; } async function getCollaborativeRecommendations( client: Client, userId: string, limit: number = 10 ): Promise<Recommendation[]> { const rows = await client.queryAll(` MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) WHERE me <> similar WITH me, similar, count(DISTINCT item) AS shared_items ORDER BY shared_items DESC LIMIT 50 MATCH (similar)-[:PURCHASED]->(recommendation:Item) WHERE NOT (me)-[:PURCHASED]->(recommendation) WITH recommendation, count(DISTINCT similar) AS recommender_count, sum(shared_items) AS score ORDER BY score DESC LIMIT $limit RETURN recommendation.id AS id, recommendation.name AS name, recommendation.category AS category, recommendation.price AS price, recommender_count AS similar_users, score `, { params: { user_id: userId, limit } }); return rows.map(row => ({ id: row.get('id')?.asString ?? '', name: row.get('name')?.asString ?? '', category: row.get('category')?.asString ?? '', price: row.get('price')?.asNumber ?? 0, similarUsers: row.get('similar_users')?.asNumber ?? 0, score: row.get('score')?.asNumber ?? 0, })); } async function main() { const client = await createClient('quic://localhost:3141'); const recs = await getCollaborativeRecommendations(client, 'user-123', 10); for (const r of recs) { console.log(`Recommended: ${r.name} (score: ${r.score.toFixed(2)})`); } await client.close(); } main(); </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880884273666828-tab-4" role="tabpanel" aria-labelledby="tabs-1774880884273666828-tab-4-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">const std = @import("std"); const geode = @import("geode_client"); const Recommendation = struct { id: []const u8, name: []const u8, category: []const u8, price: f64, similar_users: i64, score: f64, }; pub fn getCollaborativeRecommendations( client: *geode.GeodeClient, allocator: std.mem.Allocator, user_id: []const u8, limit: i64, ) !std.ArrayList(Recommendation) { var params = std.json.ObjectMap.init(allocator); defer params.deinit(); try params.put("user_id", .{ .string = user_id }); try params.put("limit", .{ .integer = limit }); try client.sendRunGql(1, \\MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) \\WHERE me <> similar \\WITH me, similar, count(DISTINCT item) AS shared_items \\ORDER BY shared_items DESC \\LIMIT 50 \\MATCH (similar)-[:PURCHASED]->(recommendation:Item) \\WHERE NOT (me)-[:PURCHASED]->(recommendation) \\WITH recommendation, count(DISTINCT similar) AS recommender_count, sum(shared_items) AS score \\ORDER BY score DESC \\LIMIT $limit \\RETURN \\ recommendation.id AS id, \\ recommendation.name AS name, \\ recommendation.category AS category, \\ recommendation.price AS price, \\ recommender_count AS similar_users, \\ score , .{ .object = params }); _ = try client.receiveMessage(30000); try client.sendPull(1, 1000); const result = try client.receiveMessage(30000); defer allocator.free(result); var recommendations = std.ArrayList(Recommendation).init(allocator); // Parse JSON result and populate recommendations return recommendations; } pub fn main() !void { var gpa = std.heap.GeneralPurposeAllocator(.{}){}; defer _ = gpa.deinit(); const allocator = gpa.allocator(); var client = geode.GeodeClient.init(allocator, "localhost", 3141, true); defer client.deinit(); try client.connect(); try client.sendHello("recommendations", "1.0.0"); _ = try client.receiveMessage(30000); const recs = try getCollaborativeRecommendations(&client, allocator, "user-123", 10); defer recs.deinit(); for (recs.items) |r| { std.debug.print("Recommended: {s} (score: {d:.2})\n", .{ r.name, r.score }); } } </code></pre></div> </div></div> </div> <h4 id="item-based-collaborative-filtering" class="position-relative d-flex align-items-center group"> Item-Based Collaborative Filtering <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="item-based-collaborative-filtering" aria-haspopup="dialog" aria-label="Share link: Item-Based Collaborative Filtering"> Share link </button> </h4>Find items frequently purchased together. <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Find items frequently purchased with items the user bought MATCH (me:User {id: $user_id})-[:PURCHASED]->(my_item:Item) MATCH (my_item)<-[:PURCHASED]-(other:User)-[:PURCHASED]->(co_purchased:Item) WHERE me <> other AND NOT (me)-[:PURCHASED]->(co_purchased) AND my_item <> co_purchased WITH co_purchased, count(DISTINCT other) AS co_purchase_count ORDER BY co_purchase_count DESC LIMIT $limit RETURN co_purchased.id AS id, co_purchased.name AS name, co_purchased.category AS category, co_purchased.price AS price, co_purchase_count AS frequency </code></pre></div> <h4 id="weighted-collaborative-filtering" class="position-relative d-flex align-items-center group"> Weighted Collaborative Filtering <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="weighted-collaborative-filtering" aria-haspopup="dialog" aria-label="Share link: Weighted Collaborative Filtering"> Share link </button> </h4>Weight different interaction types for better recommendations. <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Weight interactions: purchase=5, rating=3, view=1 MATCH (me:User {id: $user_id}) // Get weighted interactions for similar users OPTIONAL MATCH (me)-[:PURCHASED]->(p_item:Item)<-[:PURCHASED]-(similar:User) WITH me, similar, sum(5) AS purchase_weight OPTIONAL MATCH (me)-[:RATED]->(r_item:Item)<-[:RATED]-(similar) WITH me, similar, purchase_weight, sum(3) AS rating_weight OPTIONAL MATCH (me)-[:VIEWED]->(v_item:Item)<-[:VIEWED]-(similar) WHERE similar IS NOT NULL WITH similar, purchase_weight + rating_weight + sum(1) AS total_weight WHERE total_weight > 5 ORDER BY total_weight DESC LIMIT 100 // Get recommendations from similar users MATCH (similar)-[:PURCHASED|RATED|WISHLISTED]->(recommendation:Item) MATCH (me:User {id: $user_id}) WHERE NOT (me)-[:PURCHASED]->(recommendation) WITH recommendation, sum(total_weight) AS score ORDER BY score DESC LIMIT $limit RETURN recommendation.id, recommendation.name, score </code></pre></div> <h3 id="content-based-recommendations" class="position-relative d-flex align-items-center group"> Content-Based Recommendations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="content-based-recommendations" aria-haspopup="dialog" aria-label="Share link: Content-Based Recommendations"> Share link </button> </h3>Content-based filtering recommends items similar to what the user has liked before. <h4 id="feature-based-similarity" class="position-relative d-flex align-items-center group"> Feature-Based Similarity <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="feature-based-similarity" aria-haspopup="dialog" aria-label="Share link: Feature-Based Similarity"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Find items with similar features to what user has purchased MATCH (me:User {id: $user_id})-[:PURCHASED]->(purchased:Item)-[:HAS_FEATURE]->(feature:Feature) WITH me, feature, count(purchased) AS feature_frequency ORDER BY feature_frequency DESC LIMIT 20 // Find items with these features MATCH (feature)<-[:HAS_FEATURE]-(recommendation:Item) WHERE NOT (me)-[:PURCHASED]->(recommendation) AND NOT (me)-[:VIEWED]->(recommendation) WITH recommendation, sum(feature_frequency) AS feature_match_score ORDER BY feature_match_score DESC LIMIT $limit RETURN recommendation.id AS id, recommendation.name AS name, recommendation.category AS category, feature_match_score AS score </code></pre></div><div class="docs-tabs mb-4"> <ul class="nav nav-tabs" id="tabs-1774880884299881850" role="tablist"><li class="nav-item" role="presentation"> <button class="nav-link active" id="tabs-1774880884299881850-tab-0-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880884299881850-tab-0" type="button" role="tab" aria-controls="tabs-1774880884299881850-tab-0" aria-selected="true" > Go </button> </li><li class="nav-item" role="presentation"> <button class="nav-link" id="tabs-1774880884299881850-tab-1-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880884299881850-tab-1" type="button" role="tab" aria-controls="tabs-1774880884299881850-tab-1" aria-selected="false" > Python </button> </li><li class="nav-item" role="presentation"> <button class="nav-link" id="tabs-1774880884299881850-tab-2-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880884299881850-tab-2" type="button" role="tab" aria-controls="tabs-1774880884299881850-tab-2" aria-selected="false" > Rust </button> </li><li class="nav-item" role="presentation"> <button class="nav-link" id="tabs-1774880884299881850-tab-3-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880884299881850-tab-3" type="button" role="tab" aria-controls="tabs-1774880884299881850-tab-3" aria-selected="false" > Node.js </button> </li><li class="nav-item" role="presentation"> <button class="nav-link" id="tabs-1774880884299881850-tab-4-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880884299881850-tab-4" type="button" role="tab" aria-controls="tabs-1774880884299881850-tab-4" aria-selected="false" > Zig </button> </li></ul> <div class="tab-content border border-top-0 rounded-bottom p-3" id="tabs-1774880884299881850-content"><div class="tab-pane fade show active" id="tabs-1774880884299881850-tab-0" role="tabpanel" aria-labelledby="tabs-1774880884299881850-tab-0-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-go" data-lang="go">func GetContentBasedRecommendations(ctx context.Context, db *sql.DB, userID string, limit int) ([]Recommendation, error) { rows, err := db.QueryContext(ctx, ` MATCH (me:User {id: ?})-[:PURCHASED]->(purchased:Item)-[:HAS_FEATURE]->(feature:Feature) WITH me, feature, count(purchased) AS feature_frequency ORDER BY feature_frequency DESC LIMIT 20 MATCH (feature)<-[:HAS_FEATURE]-(recommendation:Item) WHERE NOT (me)-[:PURCHASED]->(recommendation) AND NOT (me)-[:VIEWED]->(recommendation) WITH recommendation, sum(feature_frequency) AS feature_match_score ORDER BY feature_match_score DESC LIMIT ? RETURN recommendation.id AS id, recommendation.name AS name, recommendation.category AS category, recommendation.price AS price, feature_match_score AS score `, userID, limit) if err != nil { return nil, err } defer rows.Close() var recommendations []Recommendation for rows.Next() { var r Recommendation err := rows.Scan(&r.ID, &r.Name, &r.Category, &r.Price, &r.Score) if err != nil { return nil, err } recommendations = append(recommendations, r) } return recommendations, nil } </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880884299881850-tab-1" role="tabpanel" aria-labelledby="tabs-1774880884299881850-tab-1-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">async def get_content_based_recommendations( client, user_id: str, limit: int = 10 ) -> List[Recommendation]: """Get recommendations based on content similarity.""" async with client.connection() as conn: result, _ = await conn.query(""" MATCH (me:User {id: $user_id})-[:PURCHASED]->(purchased:Item)-[:HAS_FEATURE]->(feature:Feature) WITH me, feature, count(purchased) AS feature_frequency ORDER BY feature_frequency DESC LIMIT 20 MATCH (feature)<-[:HAS_FEATURE]-(recommendation:Item) WHERE NOT (me)-[:PURCHASED]->(recommendation) AND NOT (me)-[:VIEWED]->(recommendation) WITH recommendation, sum(feature_frequency) AS feature_match_score ORDER BY feature_match_score DESC LIMIT $limit RETURN recommendation.id AS id, recommendation.name AS name, recommendation.category AS category, recommendation.price AS price, feature_match_score AS score """, {"user_id": user_id, "limit": limit}) return [ Recommendation( id=row['id'].as_string, name=row['name'].as_string, category=row['category'].as_string, price=row['price'].as_float, similar_users=0, score=row['score'].as_float ) for row in result.rows ] </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880884299881850-tab-2" role="tabpanel" aria-labelledby="tabs-1774880884299881850-tab-2-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-rust" data-lang="rust">async fn get_content_based_recommendations( conn: &mut geode_client::Connection, user_id: &str, limit: i64, ) -> Result<Vec<Recommendation>, Box<dyn std::error::Error>> { let mut params = HashMap::new(); params.insert("user_id".to_string(), Value::string(user_id)); params.insert("limit".to_string(), Value::int(limit)); let (page, _) = conn.query_with_params(r#" MATCH (me:User {id: $user_id})-[:PURCHASED]->(purchased:Item)-[:HAS_FEATURE]->(feature:Feature) WITH me, feature, count(purchased) AS feature_frequency ORDER BY feature_frequency DESC LIMIT 20 MATCH (feature)<-[:HAS_FEATURE]-(recommendation:Item) WHERE NOT (me)-[:PURCHASED]->(recommendation) AND NOT (me)-[:VIEWED]->(recommendation) WITH recommendation, sum(feature_frequency) AS feature_match_score ORDER BY feature_match_score DESC LIMIT $limit RETURN recommendation.id AS id, recommendation.name AS name, recommendation.category AS category, recommendation.price AS price, feature_match_score AS score "#, &params).await?; let mut recommendations = Vec::new(); for row in &page.rows { recommendations.push(Recommendation { id: row.get("id").unwrap().as_string()?, name: row.get("name").unwrap().as_string()?, category: row.get("category").unwrap().as_string()?, price: row.get("price").unwrap().as_float()?, similar_users: 0, score: row.get("score").unwrap().as_float()?, }); } Ok(recommendations) } </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880884299881850-tab-3" role="tabpanel" aria-labelledby="tabs-1774880884299881850-tab-3-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-typescript" data-lang="typescript">async function getContentBasedRecommendations( client: Client, userId: string, limit: number = 10 ): Promise<Recommendation[]> { const rows = await client.queryAll(` MATCH (me:User {id: $user_id})-[:PURCHASED]->(purchased:Item)-[:HAS_FEATURE]->(feature:Feature) WITH me, feature, count(purchased) AS feature_frequency ORDER BY feature_frequency DESC LIMIT 20 MATCH (feature)<-[:HAS_FEATURE]-(recommendation:Item) WHERE NOT (me)-[:PURCHASED]->(recommendation) AND NOT (me)-[:VIEWED]->(recommendation) WITH recommendation, sum(feature_frequency) AS feature_match_score ORDER BY feature_match_score DESC LIMIT $limit RETURN recommendation.id AS id, recommendation.name AS name, recommendation.category AS category, recommendation.price AS price, feature_match_score AS score `, { params: { user_id: userId, limit } }); return rows.map(row => ({ id: row.get('id')?.asString ?? '', name: row.get('name')?.asString ?? '', category: row.get('category')?.asString ?? '', price: row.get('price')?.asNumber ?? 0, similarUsers: 0, score: row.get('score')?.asNumber ?? 0, })); } </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880884299881850-tab-4" role="tabpanel" aria-labelledby="tabs-1774880884299881850-tab-4-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">pub fn getContentBasedRecommendations( client: *geode.GeodeClient, allocator: std.mem.Allocator, user_id: []const u8, limit: i64, ) !std.ArrayList(Recommendation) { var params = std.json.ObjectMap.init(allocator); defer params.deinit(); try params.put("user_id", .{ .string = user_id }); try params.put("limit", .{ .integer = limit }); try client.sendRunGql(1, \\MATCH (me:User {id: $user_id})-[:PURCHASED]->(purchased:Item)-[:HAS_FEATURE]->(feature:Feature) \\WITH me, feature, count(purchased) AS feature_frequency \\ORDER BY feature_frequency DESC \\LIMIT 20 \\MATCH (feature)<-[:HAS_FEATURE]-(recommendation:Item) \\WHERE NOT (me)-[:PURCHASED]->(recommendation) \\ AND NOT (me)-[:VIEWED]->(recommendation) \\WITH recommendation, sum(feature_frequency) AS feature_match_score \\ORDER BY feature_match_score DESC \\LIMIT $limit \\RETURN \\ recommendation.id AS id, \\ recommendation.name AS name, \\ recommendation.category AS category, \\ recommendation.price AS price, \\ feature_match_score AS score , .{ .object = params }); _ = try client.receiveMessage(30000); try client.sendPull(1, 1000); const result = try client.receiveMessage(30000); defer allocator.free(result); var recommendations = std.ArrayList(Recommendation).init(allocator); return recommendations; } </code></pre></div> </div></div> </div> <h4 id="category-based-recommendations" class="position-relative d-flex align-items-center group"> Category-Based Recommendations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="category-based-recommendations" aria-haspopup="dialog" aria-label="Share link: Category-Based Recommendations"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Recommend items from categories the user frequents MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)-[:IN_CATEGORY]->(category:Category) WITH me, category, count(item) AS purchase_count ORDER BY purchase_count DESC LIMIT 5 // Find popular items in those categories MATCH (category)<-[:IN_CATEGORY]-(recommendation:Item) WHERE NOT (me)-[:PURCHASED]->(recommendation) WITH recommendation, sum(purchase_count) AS category_relevance, recommendation.popularity_score AS popularity ORDER BY category_relevance * popularity DESC LIMIT $limit RETURN recommendation.id, recommendation.name, recommendation.category, category_relevance, popularity </code></pre></div> <h4 id="tag-based-recommendations" class="position-relative d-flex align-items-center group"> Tag-Based Recommendations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="tag-based-recommendations" aria-haspopup="dialog" aria-label="Share link: Tag-Based Recommendations"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Find items with similar tags to user's interests MATCH (me:User {id: $user_id})-[:PURCHASED|WISHLISTED|RATED]->(item:Item)-[:HAS_TAG]->(tag:Tag) WITH me, tag, count(item) AS tag_affinity ORDER BY tag_affinity DESC LIMIT 15 MATCH (tag)<-[:HAS_TAG]-(recommendation:Item) WHERE NOT (me)-[:PURCHASED]->(recommendation) WITH recommendation, collect(DISTINCT tag.name) AS matching_tags, sum(tag_affinity) AS tag_score WHERE size(matching_tags) >= 2 // At least 2 matching tags ORDER BY tag_score DESC LIMIT $limit RETURN recommendation.id, recommendation.name, matching_tags, tag_score </code></pre></div> <h3 id="graph-based-recommendations" class="position-relative d-flex align-items-center group"> Graph-Based Recommendations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="graph-based-recommendations" aria-haspopup="dialog" aria-label="Share link: Graph-Based Recommendations"> Share link </button> </h3>Leverage graph structure for sophisticated recommendations. <h4 id="similar-items-via-graph-distance" class="position-relative d-flex align-items-center group"> Similar Items via Graph Distance <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="similar-items-via-graph-distance" aria-haspopup="dialog" aria-label="Share link: Similar Items via Graph Distance"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Find items within 2 hops of items user liked MATCH (me:User {id: $user_id})-[r:RATED]->(liked:Item) WHERE r.rating >= 4 WITH me, liked // Traverse similar items MATCH (liked)-[:SIMILAR_TO*1..2]->(recommendation:Item) WHERE NOT (me)-[:PURCHASED]->(recommendation) AND liked <> recommendation WITH recommendation, count(DISTINCT liked) AS connection_count, min(length((liked)-[:SIMILAR_TO*]->(recommendation))) AS distance ORDER BY connection_count DESC, distance ASC LIMIT $limit RETURN recommendation.id, recommendation.name, connection_count, distance </code></pre></div> <h4 id="pagerank-style-recommendations" class="position-relative d-flex align-items-center group"> PageRank-Style Recommendations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="pagerank-style-recommendations" aria-haspopup="dialog" aria-label="Share link: PageRank-Style Recommendations"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Personalized PageRank from user's purchased items MATCH (me:User {id: $user_id})-[:PURCHASED]->(seed:Item) WITH me, collect(seed) AS seeds // Walk the graph UNWIND seeds AS seed MATCH path = (seed)-[:SIMILAR_TO|FREQUENTLY_BOUGHT_WITH*1..3]->(target:Item) WHERE NOT target IN seeds AND NOT (me)-[:PURCHASED]->(target) WITH target, count(path) AS path_count, avg(length(path)) AS avg_distance ORDER BY path_count DESC, avg_distance ASC LIMIT $limit RETURN target.id, target.name, path_count, avg_distance </code></pre></div> <h4 id="community-based-recommendations" class="position-relative d-flex align-items-center group"> Community-Based Recommendations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="community-based-recommendations" aria-haspopup="dialog" aria-label="Share link: Community-Based Recommendations"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Find users in similar "purchase communities" MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(neighbor:User) WITH me, neighbor, count(item) AS shared_items WHERE shared_items >= 3 WITH me, collect(neighbor) AS community // Find popular items within the community UNWIND community AS member MATCH (member)-[:PURCHASED]->(item:Item) WHERE NOT (me)-[:PURCHASED]->(item) WITH item, count(DISTINCT member) AS community_purchases ORDER BY community_purchases DESC LIMIT $limit RETURN item.id, item.name, community_purchases </code></pre></div> <h3 id="real-time-vs-batch-recommendations" class="position-relative d-flex align-items-center group"> Real-Time vs Batch Recommendations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="real-time-vs-batch-recommendations" aria-haspopup="dialog" aria-label="Share link: Real-Time vs Batch Recommendations"> Share link </button> </h3> <h4 id="real-time-recommendations" class="position-relative d-flex align-items-center group"> Real-Time Recommendations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="real-time-recommendations" aria-haspopup="dialog" aria-label="Share link: Real-Time Recommendations"> Share link </button> </h4>For immediate, personalized recommendations: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Quick personalized recommendations (< 100ms) MATCH (me:User {id: $user_id}) // Get recent activity OPTIONAL MATCH (me)-[r:VIEWED|PURCHASED]->(recent:Item) WHERE r.timestamp > timestamp() - duration('P7D') WITH me, collect(recent)[0..5] AS recent_items // Find similar items UNWIND recent_items AS recent MATCH (recent)-[:SIMILAR_TO]->(similar:Item) WHERE NOT (me)-[:PURCHASED]->(similar) WITH DISTINCT similar, recent.category AS source_category LIMIT $limit RETURN similar.id, similar.name, similar.category, source_category </code></pre></div><div class="docs-tabs mb-4"> <ul class="nav nav-tabs" id="tabs-1774880884314694633" role="tablist"><li class="nav-item" role="presentation"> <button class="nav-link active" id="tabs-1774880884314694633-tab-0-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880884314694633-tab-0" type="button" role="tab" aria-controls="tabs-1774880884314694633-tab-0" aria-selected="true" > Go </button> </li><li class="nav-item" role="presentation"> <button class="nav-link" id="tabs-1774880884314694633-tab-1-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880884314694633-tab-1" type="button" role="tab" 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class="tab-content border border-top-0 rounded-bottom p-3" id="tabs-1774880884314694633-content"><div class="tab-pane fade show active" id="tabs-1774880884314694633-tab-0" role="tabpanel" aria-labelledby="tabs-1774880884314694633-tab-0-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-go" data-lang="go">func GetRealTimeRecommendations(ctx context.Context, db *sql.DB, userID string, limit int) ([]Recommendation, error) { // Use context with timeout for real-time requirements ctx, cancel := context.WithTimeout(ctx, 100*time.Millisecond) defer cancel() rows, err := db.QueryContext(ctx, ` MATCH (me:User {id: ?}) OPTIONAL MATCH (me)-[r:VIEWED|PURCHASED]->(recent:Item) WHERE r.timestamp > timestamp() - duration('P7D') WITH me, collect(recent)[0..5] AS recent_items UNWIND recent_items AS recent MATCH (recent)-[:SIMILAR_TO]->(similar:Item) WHERE NOT (me)-[:PURCHASED]->(similar) WITH DISTINCT similar, recent.category AS source_category LIMIT ? RETURN similar.id, similar.name, similar.category, source_category `, userID, limit) if err != nil { // Fallback to cached recommendations on timeout if ctx.Err() == context.DeadlineExceeded { return getCachedRecommendations(userID, limit) } return nil, err } defer rows.Close() var recommendations []Recommendation for rows.Next() { var r Recommendation var sourceCategory string rows.Scan(&r.ID, &r.Name, &r.Category, &sourceCategory) recommendations = append(recommendations, r) } return recommendations, nil } </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880884314694633-tab-1" role="tabpanel" aria-labelledby="tabs-1774880884314694633-tab-1-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">import asyncio from typing import Optional async def get_realtime_recommendations( client, user_id: str, limit: int = 10, timeout_ms: int = 100 ) -> List[Recommendation]: """Get real-time recommendations with timeout fallback.""" try: async with asyncio.timeout(timeout_ms / 1000): async with client.connection() as conn: result, _ = await conn.query(""" MATCH (me:User {id: $user_id}) OPTIONAL MATCH (me)-[r:VIEWED|PURCHASED]->(recent:Item) WHERE r.timestamp > timestamp() - duration('P7D') WITH me, collect(recent)[0..5] AS recent_items UNWIND recent_items AS recent MATCH (recent)-[:SIMILAR_TO]->(similar:Item) WHERE NOT (me)-[:PURCHASED]->(similar) WITH DISTINCT similar, recent.category AS source_category LIMIT $limit RETURN similar.id, similar.name, similar.category, source_category """, {"user_id": user_id, "limit": limit}) return [ Recommendation( id=row['similar.id'].as_string, name=row['similar.name'].as_string, category=row['similar.category'].as_string, price=0, similar_users=0, score=0 ) for row in result.rows ] except asyncio.TimeoutError: # Fallback to cached recommendations return await get_cached_recommendations(user_id, limit) </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880884314694633-tab-2" role="tabpanel" aria-labelledby="tabs-1774880884314694633-tab-2-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-rust" data-lang="rust">use tokio::time::{timeout, Duration}; async fn get_realtime_recommendations( conn: &mut geode_client::Connection, user_id: &str, limit: i64, ) -> Result<Vec<Recommendation>, Box<dyn std::error::Error>> { let mut params = HashMap::new(); params.insert("user_id".to_string(), Value::string(user_id)); params.insert("limit".to_string(), Value::int(limit)); // Timeout after 100ms let result = timeout(Duration::from_millis(100), async { conn.query_with_params(r#" MATCH (me:User {id: $user_id}) OPTIONAL MATCH (me)-[r:VIEWED|PURCHASED]->(recent:Item) WHERE r.timestamp > timestamp() - duration('P7D') WITH me, collect(recent)[0..5] AS recent_items UNWIND recent_items AS recent MATCH (recent)-[:SIMILAR_TO]->(similar:Item) WHERE NOT (me)-[:PURCHASED]->(similar) WITH DISTINCT similar, recent.category AS source_category LIMIT $limit RETURN similar.id, similar.name, similar.category, source_category "#, &params).await }).await; match result { Ok(Ok((page, _))) => { let mut recommendations = Vec::new(); for row in &page.rows { recommendations.push(Recommendation { id: row.get("similar.id").unwrap().as_string()?, name: row.get("similar.name").unwrap().as_string()?, category: row.get("similar.category").unwrap().as_string()?, price: 0.0, similar_users: 0, score: 0.0, }); } Ok(recommendations) } Ok(Err(e)) => Err(e.into()), Err(_) => { // Timeout - return cached recommendations get_cached_recommendations(user_id, limit).await } } } </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880884314694633-tab-3" role="tabpanel" aria-labelledby="tabs-1774880884314694633-tab-3-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-typescript" data-lang="typescript">async function getRealtimeRecommendations( client: Client, userId: string, limit: number = 10, timeoutMs: number = 100 ): Promise<Recommendation[]> { const controller = new AbortController(); const timeoutId = setTimeout(() => controller.abort(), timeoutMs); try { const rows = await client.queryAll(` MATCH (me:User {id: $user_id}) OPTIONAL MATCH (me)-[r:VIEWED|PURCHASED]->(recent:Item) WHERE r.timestamp > timestamp() - duration('P7D') WITH me, collect(recent)[0..5] AS recent_items UNWIND recent_items AS recent MATCH (recent)-[:SIMILAR_TO]->(similar:Item) WHERE NOT (me)-[:PURCHASED]->(similar) WITH DISTINCT similar, recent.category AS source_category LIMIT $limit RETURN similar.id, similar.name, similar.category, source_category `, { params: { user_id: userId, limit }, signal: controller.signal }); return rows.map(row => ({ id: row.get('similar.id')?.asString ?? '', name: row.get('similar.name')?.asString ?? '', category: row.get('similar.category')?.asString ?? '', price: 0, similarUsers: 0, score: 0, })); } catch (error) { if (error.name === 'AbortError') { // Return cached recommendations on timeout return getCachedRecommendations(userId, limit); } throw error; } finally { clearTimeout(timeoutId); } } </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880884314694633-tab-4" role="tabpanel" aria-labelledby="tabs-1774880884314694633-tab-4-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">pub fn getRealtimeRecommendations( client: *geode.GeodeClient, allocator: std.mem.Allocator, user_id: []const u8, limit: i64, timeout_ms: u64, ) !std.ArrayList(Recommendation) { var params = std.json.ObjectMap.init(allocator); defer params.deinit(); try params.put("user_id", .{ .string = user_id }); try params.put("limit", .{ .integer = limit }); // Use timeout for real-time requirements try client.sendRunGql(1, \\MATCH (me:User {id: $user_id}) \\OPTIONAL MATCH (me)-[r:VIEWED|PURCHASED]->(recent:Item) \\WHERE r.timestamp > timestamp() - duration('P7D') \\WITH me, collect(recent)[0..5] AS recent_items \\UNWIND recent_items AS recent \\MATCH (recent)-[:SIMILAR_TO]->(similar:Item) \\WHERE NOT (me)-[:PURCHASED]->(similar) \\WITH DISTINCT similar, recent.category AS source_category \\LIMIT $limit \\RETURN similar.id, similar.name, similar.category, source_category , .{ .object = params }); const result = client.receiveMessage(timeout_ms) catch |err| { if (err == error.Timeout) { // Return cached recommendations return getCachedRecommendations(allocator, user_id, limit); } return err; }; defer allocator.free(result); var recommendations = std.ArrayList(Recommendation).init(allocator); return recommendations; } </code></pre></div> </div></div> </div> <h4 id="batch-pre-computation" class="position-relative d-flex align-items-center group"> Batch Pre-Computation <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="batch-pre-computation" aria-haspopup="dialog" aria-label="Share link: Batch Pre-Computation"> Share link </button> </h4>For complex recommendations that can be pre-computed: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Pre-compute recommendations for active users (run nightly) MATCH (user:User) WHERE user.last_active > timestamp() - duration('P30D') // Compute collaborative filtering recommendations CALL { WITH user MATCH (user)-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) WHERE user <> similar WITH user, similar, count(item) AS shared ORDER BY shared DESC LIMIT 50 MATCH (similar)-[:PURCHASED]->(rec:Item) WHERE NOT (user)-[:PURCHASED]->(rec) WITH user, rec, count(similar) AS score ORDER BY score DESC LIMIT 50 RETURN user, collect({item_id: rec.id, score: score}) AS recommendations } // Store in cache MERGE (cache:RecommendationCache {user_id: user.id}) SET cache.items = recommendations, cache.computed_at = timestamp(), cache.algorithm = 'collaborative' </code></pre></div> <h3 id="ab-testing-recommendations" class="position-relative d-flex align-items-center group"> A/B Testing Recommendations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="ab-testing-recommendations" aria-haspopup="dialog" aria-label="Share link: A/B Testing Recommendations"> Share link </button> </h3> <h4 id="experiment-setup" class="position-relative d-flex align-items-center group"> Experiment Setup <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="experiment-setup" aria-haspopup="dialog" aria-label="Share link: Experiment Setup"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Create experiment groups CREATE (:Experiment { id: 'rec-algo-test-2026-01', name: 'Recommendation Algorithm Test', start_date: date('2026-01-01'), end_date: date('2026-01-31'), variants: ['control', 'collaborative', 'content', 'hybrid'] }) // Assign users to variants (deterministic hash) MATCH (user:User) WHERE NOT (user)-[:IN_EXPERIMENT]->(:Experiment {id: 'rec-algo-test-2026-01'}) WITH user, abs(hash(user.id)) % 4 AS variant_index MATCH (exp:Experiment {id: 'rec-algo-test-2026-01'}) CREATE (user)-[:IN_EXPERIMENT { variant: exp.variants[variant_index], assigned_at: timestamp() }]->(exp) </code></pre></div> <h4 id="tracking-results" class="position-relative d-flex align-items-center group"> Tracking Results <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="tracking-results" aria-haspopup="dialog" aria-label="Share link: Tracking Results"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Track recommendation click-through MATCH (user:User {id: $user_id})-[exp:IN_EXPERIMENT]->(experiment:Experiment {id: $experiment_id}) MATCH (item:Item {id: $item_id}) CREATE (user)-[:CLICKED_RECOMMENDATION { item_id: item.id, variant: exp.variant, position: $position, timestamp: timestamp(), experiment_id: experiment.id }]->(item) </code></pre></div> <h4 id="analyze-results" class="position-relative d-flex align-items-center group"> Analyze Results <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="analyze-results" aria-haspopup="dialog" aria-label="Share link: Analyze Results"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Compare conversion rates by variant MATCH (exp:Experiment {id: $experiment_id}) MATCH (user:User)-[assignment:IN_EXPERIMENT]->(exp) // Get impressions OPTIONAL MATCH (user)-[impression:VIEWED_RECOMMENDATION]->(item:Item) WHERE impression.experiment_id = exp.id // Get clicks OPTIONAL MATCH (user)-[click:CLICKED_RECOMMENDATION]->(item:Item) WHERE click.experiment_id = exp.id // Get conversions OPTIONAL MATCH (user)-[purchase:PURCHASED]->(item:Item) WHERE purchase.timestamp > assignment.assigned_at AND (user)-[:CLICKED_RECOMMENDATION {experiment_id: exp.id}]->(item) WITH assignment.variant AS variant, count(DISTINCT user) AS users, count(DISTINCT impression) AS impressions, count(DISTINCT click) AS clicks, count(DISTINCT purchase) AS conversions RETURN variant, users, impressions, clicks, conversions, toFloat(clicks) / impressions AS ctr, toFloat(conversions) / clicks AS conversion_rate ORDER BY conversion_rate DESC </code></pre></div> <h3 id="performance-optimization" class="position-relative d-flex align-items-center group"> Performance Optimization <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="performance-optimization" aria-haspopup="dialog" aria-label="Share link: Performance Optimization"> Share link </button> </h3> <h4 id="query-optimization" class="position-relative d-flex align-items-center group"> Query Optimization <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="query-optimization" aria-haspopup="dialog" aria-label="Share link: Query Optimization"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Use LIMIT early in the query MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item) WITH me, item LIMIT 100 // Limit input set early // Continue with similarity search MATCH (item)<-[:PURCHASED]-(similar:User) WHERE me <> similar // ... </code></pre></div> <h4 id="index-usage" class="position-relative d-flex align-items-center group"> Index Usage <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="index-usage" aria-haspopup="dialog" aria-label="Share link: Index Usage"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Create indexes for recommendation queries CREATE INDEX item_popularity ON :Item(popularity_score) CREATE INDEX item_category_popularity ON :Item(category, popularity_score) CREATE INDEX user_last_active ON :User(last_active) // Composite index for filtering CREATE INDEX item_category_price ON :Item(category, price) </code></pre></div> <h4 id="caching-strategy" class="position-relative d-flex align-items-center group"> Caching Strategy <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="caching-strategy" aria-haspopup="dialog" aria-label="Share link: Caching Strategy"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Cache popular item similarities MATCH (popular:Item) WHERE popular.popularity_score > 0.8 // Pre-compute similarities MATCH (popular)<-[:PURCHASED]-(user:User)-[:PURCHASED]->(other:Item) WHERE popular <> other WITH popular, other, count(user) AS co_purchases WHERE co_purchases >= 10 ORDER BY co_purchases DESC LIMIT 20 // Store as relationship MERGE (popular)-[s:SIMILAR_TO]->(other) SET s.score = co_purchases, s.updated_at = timestamp() </code></pre></div> <h4 id="hybrid-recommendations" class="position-relative d-flex align-items-center group"> Hybrid Recommendations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="hybrid-recommendations" aria-haspopup="dialog" aria-label="Share link: Hybrid Recommendations"> Share link </button> </h4>Combine multiple approaches for best results: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Hybrid: Collaborative + Content + Popularity MATCH (me:User {id: $user_id}) // Collaborative component (40% weight) OPTIONAL MATCH (me)-[:PURCHASED]->(p:Item)<-[:PURCHASED]-(similar:User)-[:PURCHASED]->(collab:Item) WHERE NOT (me)-[:PURCHASED]->(collab) WITH me, collab, count(similar) * 0.4 AS collab_score // Content component (35% weight) OPTIONAL MATCH (me)-[:PURCHASED]->(my_item:Item)-[:HAS_FEATURE]->(f:Feature)<-[:HAS_FEATURE]-(content:Item) WHERE NOT (me)-[:PURCHASED]->(content) WITH me, collab, collab_score, content, count(f) * 0.35 AS content_score // Popularity component (25% weight) WITH me, CASE WHEN collab IS NOT NULL THEN collab ELSE content END AS item, CASE WHEN collab IS NOT NULL THEN collab_score ELSE 0 END + CASE WHEN content IS NOT NULL THEN content_score ELSE 0 END AS base_score MATCH (item) WITH item, base_score + item.popularity_score * 0.25 AS final_score ORDER BY final_score DESC LIMIT $limit RETURN item.id, item.name, final_score </code></pre></div> <h3 id="monitoring-and-metrics" class="position-relative d-flex align-items-center group"> Monitoring and Metrics <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="monitoring-and-metrics" aria-haspopup="dialog" aria-label="Share link: Monitoring and Metrics"> Share link </button> </h3> <h4 id="track-recommendation-performance" class="position-relative d-flex align-items-center group"> Track Recommendation Performance <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="track-recommendation-performance" aria-haspopup="dialog" aria-label="Share link: Track Recommendation Performance"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Create metrics for recommendation system MATCH (user:User)-[click:CLICKED_RECOMMENDATION]->(item:Item) WHERE click.timestamp > timestamp() - duration('P1D') WITH count(DISTINCT click) AS total_clicks, count(DISTINCT user) AS unique_users, avg(click.position) AS avg_click_position // Compare with purchases MATCH (user:User)-[purchase:PURCHASED]->(item:Item) WHERE purchase.timestamp > timestamp() - duration('P1D') AND (user)-[:CLICKED_RECOMMENDATION]->(item) WITH total_clicks, unique_users, avg_click_position, count(purchase) AS recommendation_purchases RETURN { total_clicks: total_clicks, unique_users: unique_users, avg_click_position: avg_click_position, recommendation_purchases: recommendation_purchases, conversion_rate: toFloat(recommendation_purchases) / total_clicks } AS metrics </code></pre></div> <h3 id="next-steps" class="position-relative d-flex align-items-center group"> Next Steps <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="next-steps" aria-haspopup="dialog" aria-label="Share link: Next Steps"> Share link </button> </h3><ul> <li><a href="/guides/social-network/" >Social Network Guide</a> - User relationship recommendations</li> <li><a href="/guides/knowledge-graph/" >Knowledge Graph Guide</a> - Entity recommendations</li> <li><a href="/guides/query-performance/" >Query Performance Guide</a> - Optimize recommendation queries</li> <li><a href="/guides/high-availability/" >High Availability Guide</a> - Scale recommendation systems</li> </ul> <h3 id="resources" class="position-relative d-flex align-items-center group"> Resources <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="resources" aria-haspopup="dialog" aria-label="Share link: Resources"> Share link </button> </h3><ul> <li><a href="/docs/gql-reference/" >GQL Reference</a> </li> <li><a href="/guides/graph-modeling/" >Graph Modeling Guide</a> </li> <li><a href="https://gitlab.com/devnw/codepros/geode/examples" aria-label="Example Applications – opens in new window" target="_blank" rel="noopener noreferrer" >Example Applications ↗ </a> </li> </ul> <hr> Questions? Join our <a href="https://forum.geodedb.com" aria-label="community forum – opens in new window" target="_blank" rel="noopener noreferrer" >community forum ↗ </a> to discuss recommendation strategies.

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