Recommendation Systems | Geode Database

<h2 id="recommendation-systems" class="position-relative d-flex align-items-center group"> Recommendation Systems <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-systems" aria-haspopup="dialog" aria-label="Share link: Recommendation Systems"> 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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} requestAnimationFrame(()=>{ input.focus(); }); trapFocus(); } function closeModal(){ if(!isOpen()) return; modal.setAttribute('hidden',''); if(lastFocus && typeof lastFocus.focus==='function') lastFocus.focus(); } function copyCurrent(){ try{ navigator.clipboard.writeText(input.value).then(()=>feedback(true),()=>fallback()); } catch(e){ fallback(); } } function fallback(){ input.select(); try{ document.execCommand('copy'); feedback(true);}catch(e){ feedback(false);} } function feedback(ok){ if(!copyBtn) return; const icon=copyBtn.querySelector('i'); if(!icon) return; const prev=copyBtn.getAttribute('data-prev')||icon.className; if(!copyBtn.getAttribute('data-prev')) copyBtn.setAttribute('data-prev',prev); icon.className= ok ? '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>Build scalable, real-time recommendation engines using graph traversal, ML embeddings, and vector similarity search. <h3 id="problem-statement" class="position-relative d-flex align-items-center group"> Problem Statement <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="problem-statement" aria-haspopup="dialog" aria-label="Share link: Problem Statement"> Share link </button> </h3>Recommendation systems predict user preferences based on: <ul> <li>Collaborative filtering: Users with similar tastes like similar items</li> <li>Content-based: Items with similar features are recommended</li> <li>Graph-based: Relationships reveal preferences (friends’ likes, purchase patterns)</li> </ul> Graph databases excel at traversing relationships and computing similarity. <h3 id="graph-model" class="position-relative d-flex align-items-center group"> Graph 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="graph-model" aria-haspopup="dialog" aria-label="Share link: Graph Model"> Share link </button> </h3><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">CREATE GRAPH RecommendationEngine; USE RecommendationEngine; -- Nodes (:User { id: UUID, name: String, demographics: JSONB, embedding: VectorF32 -- User behavior embedding }) (:Item { id: UUID, name: String, category: String, price: Decimal, features: JSONB, embedding: VectorF32 -- Item feature embedding }) (:Category { name: String }) -- Relationships (:User)-[:VIEWED {timestamp: Timestamp}]->(:Item) (:User)-[:PURCHASED {timestamp: Timestamp, rating: Int}]->(:Item) (:User)-[:RATED {score: Float, timestamp: Timestamp}]->(:Item) (:User)-[:FOLLOWS]->(:User) (:Item)-[:IN_CATEGORY]->(:Category) (:Item)-[:SIMILAR_TO {score: Float}]->(:Item) </code></pre></div> <h3 id="recommendation-strategies" class="position-relative d-flex align-items-center group"> Recommendation Strategies <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-strategies" aria-haspopup="dialog" aria-label="Share link: Recommendation Strategies"> Share link </button> </h3> <h4 id="1-collaborative-filtering-user-user" class="position-relative d-flex align-items-center group"> 1. Collaborative Filtering (User-User) <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="1-collaborative-filtering-user-user" aria-haspopup="dialog" aria-label="Share link: 1. Collaborative Filtering (User-User)"> Share link </button> </h4>Recommend items liked by similar users: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Find users similar to current user and their recommendations MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) WITH similar, COUNT(item) AS common_purchases ORDER BY common_purchases DESC LIMIT 10 MATCH (similar)-[:PURCHASED]->(recommended:Item) WHERE NOT EXISTS { MATCH (me:User {id: $user_id})-[:PURCHASED]->(recommended) } WITH recommended, COUNT(similar) AS recommendation_strength RETURN recommended.name, recommendation_strength ORDER BY recommendation_strength DESC LIMIT 20; </code></pre></div><div class="docs-tabs mb-4"> <ul class="nav nav-tabs" id="tabs-1774880882728753719" role="tablist"><li class="nav-item" role="presentation"> <button class="nav-link active" id="tabs-1774880882728753719-tab-0-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880882728753719-tab-0" type="button" role="tab" aria-controls="tabs-1774880882728753719-tab-0" aria-selected="true" > Go </button> </li><li class="nav-item" role="presentation"> <button class="nav-link" id="tabs-1774880882728753719-tab-1-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880882728753719-tab-1" type="button" role="tab" aria-controls="tabs-1774880882728753719-tab-1" aria-selected="false" > Python </button> </li><li class="nav-item" role="presentation"> <button class="nav-link" id="tabs-1774880882728753719-tab-2-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880882728753719-tab-2" type="button" role="tab" aria-controls="tabs-1774880882728753719-tab-2" aria-selected="false" > Rust </button> </li><li class="nav-item" role="presentation"> <button class="nav-link" id="tabs-1774880882728753719-tab-3-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880882728753719-tab-3" type="button" role="tab" aria-controls="tabs-1774880882728753719-tab-3" aria-selected="false" > Node.js </button> </li><li class="nav-item" role="presentation"> <button class="nav-link" id="tabs-1774880882728753719-tab-4-tab" data-bs-toggle="tab" data-bs-target="#tabs-1774880882728753719-tab-4" type="button" role="tab" aria-controls="tabs-1774880882728753719-tab-4" aria-selected="false" > Zig </button> </li></ul> <div class="tab-content border border-top-0 rounded-bottom p-3" id="tabs-1774880882728753719-content"><div class="tab-pane fade show active" id="tabs-1774880882728753719-tab-0" role="tabpanel" aria-labelledby="tabs-1774880882728753719-tab-0-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-go" data-lang="go">func getCollaborativeRecommendations(ctx context.Context, db *sql.DB, userID string) ([]Recommendation, error) { rows, err := db.QueryContext(ctx, ` MATCH (me:User {id: ?})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) WITH similar, COUNT(item) AS common ORDER BY common DESC LIMIT 10 MATCH (similar)-[:PURCHASED]->(rec:Item) WHERE NOT EXISTS { MATCH (me:User {id: ?})-[:PURCHASED]->(rec) } WITH rec, COUNT(similar) AS strength RETURN rec.name, rec.category, strength ORDER BY strength DESC LIMIT 20 `, userID, userID) if err != nil { return nil, err } defer rows.Close() var recs []Recommendation for rows.Next() { var r Recommendation rows.Scan(&r.Name, &r.Category, &r.Score) recs = append(recs, r) } return recs, nil } </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880882728753719-tab-1" role="tabpanel" aria-labelledby="tabs-1774880882728753719-tab-1-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">async def get_collaborative_recommendations(conn, user_id: str, limit: int = 20): page, _ = await conn.query(""" MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) WITH similar, COUNT(item) AS common ORDER BY common DESC LIMIT 10 MATCH (similar)-[:PURCHASED]->(rec:Item) WHERE NOT EXISTS { MATCH (me:User {id: $user_id})-[:PURCHASED]->(rec) } WITH rec, COUNT(similar) AS strength RETURN rec.name, rec.category, strength ORDER BY strength DESC LIMIT $limit """, {"user_id": user_id, "limit": limit}) return [ {"name": row["rec.name"].as_string, "strength": row["strength"].as_int} for row in page.rows ] </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880882728753719-tab-2" role="tabpanel" aria-labelledby="tabs-1774880882728753719-tab-2-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-rust" data-lang="rust">async fn get_collaborative_recommendations(conn: &mut Connection, user_id: &str) -> Result<Vec<Recommendation>> { let params = [("user_id", Value::string(user_id))].into(); let (page, _) = conn.query_with_params(r#" MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) WITH similar, COUNT(item) AS common ORDER BY common DESC LIMIT 10 MATCH (similar)-[:PURCHASED]->(rec:Item) WHERE NOT EXISTS { MATCH (me:User {id: $user_id})-[:PURCHASED]->(rec) } WITH rec, COUNT(similar) AS strength RETURN rec.name, rec.category, strength ORDER BY strength DESC LIMIT 20 "#, &params).await?; Ok(page.rows.iter().map(|row| Recommendation { name: row.get("rec.name").unwrap().as_string().unwrap(), category: row.get("rec.category").unwrap().as_string().unwrap(), score: row.get("strength").unwrap().as_int().unwrap() as f64, }).collect()) } </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880882728753719-tab-3" role="tabpanel" aria-labelledby="tabs-1774880882728753719-tab-3-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-typescript" data-lang="typescript">async function getCollaborativeRecommendations(userId: string, limit = 20) { const rows = await client.queryAll(` MATCH (me:User {id: $userId})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) WITH similar, COUNT(item) AS common ORDER BY common DESC LIMIT 10 MATCH (similar)-[:PURCHASED]->(rec:Item) WHERE NOT EXISTS { MATCH (me:User {id: $userId})-[:PURCHASED]->(rec) } WITH rec, COUNT(similar) AS strength RETURN rec.name, rec.category, strength ORDER BY strength DESC LIMIT $limit `, { params: { userId, limit } }); return rows.map(row => ({ name: row.get('rec.name')?.asString, category: row.get('rec.category')?.asString, strength: row.get('strength')?.asNumber })); } </code></pre></div> </div><div class="tab-pane fade" id="tabs-1774880882728753719-tab-4" role="tabpanel" aria-labelledby="tabs-1774880882728753719-tab-4-tab" > <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">fn getCollaborativeRecommendations(client: *GeodeClient, allocator: std.mem.Allocator, user_id: []const u8) ![]Recommendation { // Build params JSON object var params = std.json.ObjectMap.init(allocator); defer params.deinit(); try params.put("user_id", .{ .string = user_id }); const query = \\MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User) \\WITH similar, COUNT(item) AS common ORDER BY common DESC LIMIT 10 \\MATCH (similar)-[:PURCHASED]->(rec:Item) \\WHERE NOT EXISTS { MATCH (me:User {id: $user_id})-[:PURCHASED]->(rec) } \\WITH rec, COUNT(similar) AS strength \\RETURN rec.name, strength ORDER BY strength DESC LIMIT 20 ; try client.sendRunGql(1, query, .{ .object = params }); const schema = try client.receiveMessage(30000); allocator.free(schema); try client.sendPull(1, 1000); const result = try client.receiveMessage(30000); defer allocator.free(result); return parseRecommendations(allocator, result); } </code></pre></div> </div></div> </div> <h4 id="2-collaborative-filtering-item-item" class="position-relative d-flex align-items-center group"> 2. Collaborative Filtering (Item-Item) <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="2-collaborative-filtering-item-item" aria-haspopup="dialog" aria-label="Share link: 2. Collaborative Filtering (Item-Item)"> Share link </button> </h4>Recommend items similar to user’s purchases: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Find items similar to what user has purchased MATCH (me:User {id: $user_id})-[:PURCHASED]->(purchased:Item) MATCH (purchased)<-[:PURCHASED]-(other:User)-[:PURCHASED]->(recommended:Item) WHERE NOT EXISTS { MATCH (me)-[:PURCHASED]->(recommended) } WITH recommended, COUNT(DISTINCT other) AS co_purchase_count RETURN recommended.name, co_purchase_count ORDER BY co_purchase_count DESC LIMIT 20; </code></pre></div> <h4 id="3-social-recommendations" class="position-relative d-flex align-items-center group"> 3. Social 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="3-social-recommendations" aria-haspopup="dialog" aria-label="Share link: 3. Social Recommendations"> Share link </button> </h4>Recommend based on what friends like: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Items purchased by friends MATCH (me:User {id: $user_id})-[:FOLLOWS]->(friend:User)-[:PURCHASED]->(item:Item) WHERE NOT EXISTS { MATCH (me)-[:PURCHASED]->(item) } WITH item, COLLECT(friend.name) AS friends_who_bought, COUNT(friend) AS friend_count RETURN item.name, friends_who_bought, friend_count ORDER BY friend_count DESC LIMIT 20; </code></pre></div> <h4 id="4-content-based-category-preferences" class="position-relative d-flex align-items-center group"> 4. Content-Based (Category Preferences) <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="4-content-based-category-preferences" aria-haspopup="dialog" aria-label="Share link: 4. Content-Based (Category Preferences)"> Share link </button> </h4>Recommend items in categories the user likes: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Find user's preferred categories MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)-[:IN_CATEGORY]->(cat:Category) WITH cat, COUNT(item) AS purchase_count ORDER BY purchase_count DESC LIMIT 3 -- Recommend popular items in preferred categories MATCH (cat)<-[:IN_CATEGORY]-(recommended:Item) WHERE NOT EXISTS { MATCH (me:User {id: $user_id})-[:PURCHASED]->(recommended) } WITH recommended, COUNT {MATCH (:User)-[:PURCHASED]->(recommended)} AS popularity RETURN recommended.name, recommended.category, popularity ORDER BY popularity DESC LIMIT 20; </code></pre></div> <h3 id="embedding-based-recommendations" class="position-relative d-flex align-items-center group"> Embedding-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="embedding-based-recommendations" aria-haspopup="dialog" aria-label="Share link: Embedding-Based Recommendations"> Share link </button> </h3> <h4 id="generate-item-embeddings" class="position-relative d-flex align-items-center group"> Generate Item Embeddings <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="generate-item-embeddings" aria-haspopup="dialog" aria-label="Share link: Generate Item Embeddings"> Share link </button> </h4>Use Node2Vec to create item embeddings based on co-purchase patterns: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Generate item embeddings CALL graph.node2vec('RecommendationEngine', { relationship_type: 'PURCHASED', dimensions: 128, walk_length: 80, num_walks: 10, p: 1.0, q: 1.0 }) YIELD node, embedding WITH node, embedding MATCH (item:Item) WHERE item.id = node.id SET item.embedding = embedding; </code></pre></div> <h4 id="create-vector-index" class="position-relative d-flex align-items-center group"> Create Vector Index <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="create-vector-index" aria-haspopup="dialog" aria-label="Share link: Create Vector Index"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">CREATE INDEX item_embedding_idx ON Item(embedding) USING vector; </code></pre></div> <h4 id="similarity-based-recommendations" class="position-relative d-flex align-items-center group"> Similarity-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="similarity-based-recommendations" aria-haspopup="dialog" aria-label="Share link: Similarity-Based Recommendations"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Find items similar to user's recent purchases MATCH (me:User {id: $user_id})-[p:PURCHASED]->(item:Item) WHERE p.timestamp > timestamp() - interval('P30D') -- Last 30 days WITH COLLECT(item.embedding) AS recent_embeddings -- Compute average embedding (user preference vector) WITH [e IN recent_embeddings | e] AS embeddings CALL vector.average(embeddings) YIELD result AS user_embedding -- Find similar items MATCH (recommended:Item) WHERE NOT EXISTS { MATCH (me:User {id: $user_id})-[:PURCHASED]->(recommended) } AND vector_distance_cosine(recommended.embedding, user_embedding) < 0.5 RETURN recommended.name, recommended.category, vector_distance_cosine(recommended.embedding, user_embedding) AS similarity ORDER BY similarity ASC LIMIT 20; </code></pre></div> <h3 id="hybrid-recommendation-system" class="position-relative d-flex align-items-center group"> Hybrid Recommendation System <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-recommendation-system" aria-haspopup="dialog" aria-label="Share link: Hybrid Recommendation System"> Share link </button> </h3>Combine multiple strategies with weighted scoring: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Collaborative filtering score MATCH (me:User {id: $user_id})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User)-[:PURCHASED]->(recommended:Item) WHERE NOT EXISTS { MATCH (me)-[:PURCHASED]->(recommended) } WITH recommended, COUNT(DISTINCT similar) AS collab_score -- Social score OPTIONAL MATCH (me:User {id: $user_id})-[:FOLLOWS]->(friend:User)-[:PURCHASED]->(recommended) WITH recommended, collab_score, COUNT(DISTINCT friend) AS social_score -- Similarity score (requires embeddings) OPTIONAL MATCH (me)-[:PURCHASED]->(purchased:Item) WITH recommended, collab_score, social_score, AVG(vector_distance_cosine(purchased.embedding, recommended.embedding)) AS avg_similarity -- Weighted final score WITH recommended, (collab_score * 0.5) + (social_score * 0.3) + ((1 - avg_similarity) * 0.2) AS final_score RETURN recommended.name, final_score ORDER BY final_score DESC LIMIT 20; </code></pre></div> <h3 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> </h3> <h4 id="update-embeddings-on-new-purchases" class="position-relative d-flex align-items-center group"> Update Embeddings on New Purchases <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="update-embeddings-on-new-purchases" aria-haspopup="dialog" aria-label="Share link: Update Embeddings on New Purchases"> Share link </button> </h4>Use CDC to trigger embedding updates: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-yaml" data-lang="yaml"># cdc-config.yaml cdc: enabled: true webhooks: - url: "https://recommendation-service.example.com/update-embeddings" events: ["edge.created"] filter: "type = 'PURCHASED'" </code></pre></div> <h4 id="incremental-embedding-updates" class="position-relative d-flex align-items-center group"> Incremental Embedding Updates <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="incremental-embedding-updates" aria-haspopup="dialog" aria-label="Share link: Incremental Embedding Updates"> Share link </button> </h4>Instead of full recomputation, update embeddings incrementally: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"># Pseudo-code for webhook handler async def on_purchase(event): user_id = event['edge']['from_node'] item_id = event['edge']['to_node'] # Fetch current embeddings user_emb = await get_user_embedding(user_id) item_emb = await get_item_embedding(item_id) # Update user embedding (weighted average with new item) alpha = 0.1 # Learning rate new_user_emb = (1 - alpha) * user_emb + alpha * item_emb # Persist updated embedding await update_user_embedding(user_id, new_user_emb) </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="index-strategy" class="position-relative d-flex align-items-center group"> Index 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="index-strategy" aria-haspopup="dialog" aria-label="Share link: Index Strategy"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Index for user lookups CREATE INDEX user_id_idx ON User(id) USING hash; -- Index for temporal queries CREATE INDEX purchase_timestamp_idx ON PURCHASED(timestamp) USING btree; -- Vector index for similarity CREATE INDEX item_embedding_idx ON Item(embedding) USING vector; CREATE INDEX user_embedding_idx ON User(embedding) USING vector; -- Category index CREATE INDEX item_category_idx ON Item(category) USING btree; </code></pre></div> <h4 id="materialized-recommendations" class="position-relative d-flex align-items-center group"> Materialized 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="materialized-recommendations" aria-haspopup="dialog" aria-label="Share link: Materialized Recommendations"> Share link </button> </h4>Pre-compute recommendations for active users: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Batch job (run nightly) MATCH (user:User) WHERE EXISTS {MATCH (user)-[:PURCHASED]->(:Item)} -- Compute top 50 recommendations CALL recommend.hybrid(user.id, {limit: 50}) YIELD item, score -- Store in user property WITH user, COLLECT({item_id: item.id, score: score}) AS recommendations SET user.cached_recommendations = recommendations::jsonb; -- Real-time query uses cache MATCH (user:User {id: $user_id}) RETURN user.cached_recommendations; </code></pre></div> <h3 id="evaluation-metrics" class="position-relative d-flex align-items-center group"> Evaluation 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="evaluation-metrics" aria-haspopup="dialog" aria-label="Share link: Evaluation Metrics"> Share link </button> </h3> <h4 id="precision-and-recall" class="position-relative d-flex align-items-center group"> Precision and Recall <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="precision-and-recall" aria-haspopup="dialog" aria-label="Share link: Precision and Recall"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Precision@K: % of recommended items that user purchased MATCH (user:User {id: $user_id}) WITH user, user.cached_recommendations AS recommendations MATCH (user)-[:PURCHASED]->(purchased:Item) WHERE purchased.id IN [r IN recommendations | r.item_id][0..20] -- Top 20 WITH COUNT(purchased) AS hits, 20 AS k RETURN hits / k AS precision_at_20; </code></pre></div> <h4 id="click-through-rate-ctr" class="position-relative d-flex align-items-center group"> Click-Through Rate (CTR) <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="click-through-rate-ctr" aria-haspopup="dialog" aria-label="Share link: Click-Through Rate (CTR)"> Share link </button> </h4>Track recommendation clicks via CDC: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-yaml" data-lang="yaml">cdc: webhooks: - url: "https://analytics.example.com/track-click" events: ["edge.created"] filter: "type = 'VIEWED'" </code></pre></div> <h3 id="complete-example" class="position-relative d-flex align-items-center group"> Complete Example <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="complete-example" aria-haspopup="dialog" aria-label="Share link: Complete Example"> Share link </button> </h3><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Seed data CREATE (:User {id: gen_random_uuid(), name: "Alice"}), (:User {id: gen_random_uuid(), name: "Bob"}), (:User {id: gen_random_uuid(), name: "Charlie"}); CREATE (:Item {id: gen_random_uuid(), name: "Laptop", category: "Electronics"}), (:Item {id: gen_random_uuid(), name: "Mouse", category: "Electronics"}), (:Item {id: gen_random_uuid(), name: "Desk Chair", category: "Furniture"}), (:Item {id: gen_random_uuid(), name: "Monitor", category: "Electronics"}); -- Alice and Bob purchase Laptop MATCH (alice:User {name: "Alice"}), (laptop:Item {name: "Laptop"}) CREATE (alice)-[:PURCHASED {timestamp: timestamp()}]->(laptop); MATCH (bob:User {name: "Bob"}), (laptop:Item {name: "Laptop"}) CREATE (bob)-[:PURCHASED {timestamp: timestamp()}]->(laptop); -- Bob also purchases Mouse MATCH (bob:User {name: "Bob"}), (mouse:Item {name: "Mouse"}) CREATE (bob)-[:PURCHASED {timestamp: timestamp()}]->(mouse); -- Recommend Mouse to Alice (collaborative filtering) MATCH (alice:User {name: "Alice"})-[:PURCHASED]->(item:Item)<-[:PURCHASED]-(similar:User)-[:PURCHASED]->(recommended:Item) WHERE NOT EXISTS { MATCH (alice)-[:PURCHASED]->(recommended) } WITH recommended, COUNT(similar) AS score RETURN recommended.name, score ORDER BY score DESC; -- Returns: Mouse (score: 1) </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="/docs/analytics/graph-algorithms" >Graph Algorithms</a> - Embedding generation</li> <li><a href="/docs/query/indexing-and-optimization" >Indexing and Optimization</a> - Vector indexes</li> <li><a href="/docs/analytics/real-time-analytics/" >Real-Time Analytics</a> - CDC integration</li> <li><a href="/docs/model/data-model-and-types" >Data Model and Types</a> - Vector types</li> </ul>