Recommendation Systems

<h2 id="recommendation-systems-in-geode" class="position-relative d-flex align-items-center group"> Recommendation Systems in Geode <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-in-geode" aria-haspopup="dialog" aria-label="Share link: Recommendation Systems in Geode"> 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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Geode’s native graph traversal capabilities enable sophisticated recommendation algorithms that consider multi-hop relationships and complex similarity metrics. <h3 id="understanding-graph-based-recommendations" class="position-relative d-flex align-items-center group"> Understanding 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="understanding-graph-based-recommendations" aria-haspopup="dialog" aria-label="Share link: Understanding Graph-Based Recommendations"> Share link </button> </h3>Graph databases excel at recommendations because they naturally model the relationships between users, items, and attributes that drive recommendation quality. Instead of computing recommendations from flat tables, you traverse the graph to discover relevant connections. <h4 id="core-recommendation-types" class="position-relative d-flex align-items-center group"> Core Recommendation Types <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-recommendation-types" aria-haspopup="dialog" aria-label="Share link: Core Recommendation Types"> Share link </button> </h4>Collaborative Filtering: Recommend items based on similar users’ behavior (“users like you also liked…”). Content-Based Filtering: Recommend items similar to those a user previously liked based on item attributes. Hybrid Approaches: Combine collaborative and content-based methods for better accuracy and coverage. Social Recommendations: Leverage social network connections to recommend items friends engaged with. <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 finds users with similar tastes and recommends items those similar users enjoyed. <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><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Find similar users and their preferences MATCH (target_user:User {id: $user_id})-[:RATED]->(item:Product)<-[:RATED]-(similar_user:User) WHERE target_user <> similar_user WITH similar_user, COUNT(DISTINCT item) AS common_items, COLLECT(DISTINCT item) AS shared_products WHERE common_items >= 3 MATCH (similar_user)-[r:RATED]->(recommendation:Product) WHERE NOT EXISTS { (target_user)-[:RATED]->(recommendation) } AND r.rating >= 4 RETURN recommendation.id AS product_id, recommendation.name AS product_name, COUNT(DISTINCT similar_user) AS recommended_by, AVG(r.rating) AS avg_rating, common_items ORDER BY recommended_by DESC, avg_rating DESC LIMIT 20; </code></pre></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><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Recommend items similar to those user already likes MATCH (user:User {id: $user_id})-[r:RATED]->(liked:Product) WHERE r.rating >= 4 MATCH (liked)<-[:RATED]-(other_user:User)-[:RATED]->(recommendation:Product) WHERE NOT EXISTS { (user)-[:RATED]->(recommendation) } WITH recommendation, COUNT(DISTINCT other_user) AS co_occurrence, AVG(CASE WHEN r2.rating IS NOT NULL THEN r2.rating ELSE 0 END) AS avg_rating ORDER BY co_occurrence DESC, avg_rating DESC RETURN recommendation.id, recommendation.name, recommendation.category, co_occurrence AS similarity_score, avg_rating LIMIT 20; </code></pre></div> <h4 id="weighted-similarity-scoring" class="position-relative d-flex align-items-center group"> Weighted Similarity Scoring <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-similarity-scoring" aria-haspopup="dialog" aria-label="Share link: Weighted Similarity Scoring"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Calculate Jaccard similarity between users MATCH (user1:User {id: $user_id})-[:RATED]->(item:Product)<-[:RATED]-(user2:User) WITH user1, user2, COUNT(DISTINCT item) AS intersection MATCH (user1)-[:RATED]->(item1:Product) WITH user1, user2, intersection, COUNT(DISTINCT item1) AS user1_items MATCH (user2)-[:RATED]->(item2:Product) WITH user1, user2, intersection, user1_items, COUNT(DISTINCT item2) AS user2_items, (user1_items + user2_items - intersection) AS union_count WITH user2, intersection * 1.0 / union_count AS jaccard_similarity WHERE jaccard_similarity > 0.2 MATCH (user2)-[r:RATED]->(rec:Product) WHERE NOT EXISTS { (user1)-[:RATED]->(rec) } AND r.rating >= 4 RETURN rec.id, rec.name, jaccard_similarity, COUNT(*) AS recommendation_count ORDER BY jaccard_similarity DESC, recommendation_count DESC; </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 with similar attributes to those the user liked. <h4 id="attribute-similarity" class="position-relative d-flex align-items-center group"> Attribute 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="attribute-similarity" aria-haspopup="dialog" aria-label="Share link: Attribute Similarity"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Recommend products with similar attributes MATCH (user:User {id: $user_id})-[r:PURCHASED]->(purchased:Product) WHERE r.timestamp > CURRENT_TIMESTAMP - INTERVAL '90' DAY MATCH (purchased)-[:HAS_CATEGORY]->(category:Category)<-[:HAS_CATEGORY]-(similar:Product) WHERE NOT EXISTS { (user)-[:PURCHASED]->(similar) } WITH similar, COUNT(DISTINCT category) AS shared_categories, COLLECT(DISTINCT category.name) AS categories MATCH (similar)-[:HAS_TAG]->(tag:Tag) WHERE EXISTS { MATCH (user)-[:PURCHASED]->(:Product)-[:HAS_TAG]->(tag) } WITH similar, shared_categories, COUNT(DISTINCT tag) AS shared_tags RETURN similar.id, similar.name, shared_categories, shared_tags, (shared_categories * 2 + shared_tags) AS similarity_score ORDER BY similarity_score DESC LIMIT 20; </code></pre></div> <h4 id="vector-similarity" class="position-relative d-flex align-items-center group"> Vector 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="vector-similarity" aria-haspopup="dialog" aria-label="Share link: Vector Similarity"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Use embeddings for content similarity MATCH (user:User {id: $user_id})-[:LIKED]->(item:Item) WITH AVG([i IN COLLECT(item) | i.embedding]) AS user_profile_vector MATCH (candidate:Item) WHERE NOT EXISTS { (user)-[:LIKED]->(candidate) } WITH candidate, COSINE_SIMILARITY(user_profile_vector, candidate.embedding) AS similarity WHERE similarity > 0.7 RETURN candidate.id, candidate.title, similarity ORDER BY similarity DESC LIMIT 20; </code></pre></div> <h3 id="social-recommendations" class="position-relative d-flex align-items-center group"> 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="social-recommendations" aria-haspopup="dialog" aria-label="Share link: Social Recommendations"> Share link </button> </h3>Leverage social network connections for recommendations. <h4 id="friend-based-recommendations" class="position-relative d-flex align-items-center group"> Friend-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="friend-based-recommendations" aria-haspopup="dialog" aria-label="Share link: Friend-Based Recommendations"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Recommend items friends engaged with MATCH (user:User {id: $user_id})-[:FOLLOWS]->(friend:User)-[interaction:LIKED|PURCHASED]->(item:Product) WHERE NOT EXISTS { (user)-[:LIKED|PURCHASED]->(item) } AND interaction.timestamp > CURRENT_TIMESTAMP - INTERVAL '30' DAY WITH item, COUNT(DISTINCT friend) AS friend_count, COLLECT(DISTINCT friend.name)[..5] AS engaging_friends, MAX(interaction.timestamp) AS most_recent RETURN item.id, item.name, friend_count, engaging_friends, most_recent ORDER BY friend_count DESC, most_recent DESC LIMIT 20; </code></pre></div> <h4 id="influencer-recommendations" class="position-relative d-flex align-items-center group"> Influencer 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="influencer-recommendations" aria-haspopup="dialog" aria-label="Share link: Influencer Recommendations"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Weight recommendations by friend influence MATCH (user:User {id: $user_id})-[:FOLLOWS]->(friend:User) WITH friend, COUNT { (friend)<-[:FOLLOWS]-() } AS follower_count, COUNT { (user)-[:FOLLOWS]->() } AS user_following_count WITH friend, LOG(1 + follower_count) / LOG(1 + user_following_count) AS influence_weight MATCH (friend)-[r:RATED]->(item:Product) WHERE NOT EXISTS { (user)-[:RATED]->(item) } AND r.rating >= 4 WITH item, SUM(r.rating * influence_weight) AS weighted_score, COUNT(DISTINCT friend) AS recommendation_count RETURN item.id, item.name, weighted_score, recommendation_count ORDER BY weighted_score DESC LIMIT 20; </code></pre></div> <h3 id="context-aware-recommendations" class="position-relative d-flex align-items-center group"> Context-Aware 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="context-aware-recommendations" aria-haspopup="dialog" aria-label="Share link: Context-Aware Recommendations"> Share link </button> </h3>Incorporate contextual signals like time, location, and session behavior. <h4 id="session-based-recommendations" class="position-relative d-flex align-items-center group"> Session-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="session-based-recommendations" aria-haspopup="dialog" aria-label="Share link: Session-Based Recommendations"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Recommend based on current session behavior MATCH (user:User {id: $user_id})-[view:VIEWED]->(recent:Product) WHERE view.timestamp > CURRENT_TIMESTAMP - INTERVAL '1' HOUR WITH COLLECT(DISTINCT recent) AS session_items MATCH (session_item:Product)<-[:VIEWED]-(other:User)-[:PURCHASED]->(next:Product) WHERE session_item IN session_items AND NOT EXISTS { (user)-[:PURCHASED]->(next) } WITH next, COUNT(*) AS co_view_purchase_count, AVG(CASE WHEN r.rating IS NOT NULL THEN r.rating ELSE 0 END) AS avg_rating RETURN next.id, next.name, co_view_purchase_count AS relevance_score, avg_rating ORDER BY relevance_score DESC, avg_rating DESC LIMIT 10; </code></pre></div> <h4 id="time-aware-recommendations" class="position-relative d-flex align-items-center group"> Time-Aware 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="time-aware-recommendations" aria-haspopup="dialog" aria-label="Share link: Time-Aware Recommendations"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Recommend seasonally relevant items MATCH (user:User {id: $user_id})-[:PURCHASED]->(past:Product) WHERE EXTRACT(MONTH FROM past.purchase_timestamp) = EXTRACT(MONTH FROM CURRENT_TIMESTAMP) WITH COLLECT(DISTINCT past.category) AS seasonal_categories MATCH (rec:Product) WHERE rec.category IN seasonal_categories AND NOT EXISTS { (user)-[:PURCHASED]->(rec) } AND rec.in_stock = true WITH rec, COUNT { (other:User)-[p:PURCHASED]->(rec) WHERE EXTRACT(MONTH FROM p.timestamp) = EXTRACT(MONTH FROM CURRENT_TIMESTAMP) } AS seasonal_popularity RETURN rec.id, rec.name, rec.category, seasonal_popularity ORDER BY seasonal_popularity DESC LIMIT 20; </code></pre></div> <h3 id="personalized-pagerank-for-recommendations" class="position-relative d-flex align-items-center group"> Personalized PageRank for 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="personalized-pagerank-for-recommendations" aria-haspopup="dialog" aria-label="Share link: Personalized PageRank for Recommendations"> Share link </button> </h3>Use personalized PageRank to find items relevant to a user’s interests. <h4 id="computing-personalized-recommendations" class="position-relative d-flex align-items-center group"> Computing Personalized 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="computing-personalized-recommendations" aria-haspopup="dialog" aria-label="Share link: Computing Personalized Recommendations"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Initialize personalized PageRank from user's interests MATCH (user:User {id: $user_id})-[:INTERESTED_IN]->(seed:Topic) MATCH (item:Item)-[:TAGGED_WITH]->(topic:Topic) SET item.ppr = CASE WHEN topic IN [seed] THEN 1.0 / COUNT { (user)-[:INTERESTED_IN]->() } ELSE 0.0 END; -- Propagate scores through item relationships MATCH (source:Item)-[r:RELATED_TO]->(target:Item) WITH target, SUM(source.ppr / COUNT { (source)-[:RELATED_TO]->() }) AS incoming_score SET target.ppr_new = 0.15 * target.ppr + 0.85 * incoming_score; -- Get top recommendations MATCH (item:Item) WHERE item.ppr_new > 0.01 AND NOT EXISTS { (user:User {id: $user_id})-[:VIEWED|PURCHASED]->(item) } RETURN item.id, item.title, item.ppr_new AS relevance_score ORDER BY relevance_score DESC LIMIT 20; </code></pre></div> <h3 id="diversity-and-serendipity" class="position-relative d-flex align-items-center group"> Diversity and Serendipity <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="diversity-and-serendipity" aria-haspopup="dialog" aria-label="Share link: Diversity and Serendipity"> Share link </button> </h3>Balance relevance with diversity to avoid filter bubbles. <h4 id="category-diversification" class="position-relative d-flex align-items-center group"> Category Diversification <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-diversification" aria-haspopup="dialog" aria-label="Share link: Category Diversification"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Ensure recommendations span multiple categories MATCH (user:User {id: $user_id}) CALL { MATCH (user)-[:RATED]->(item:Product) WHERE item.rating >= 4 MATCH (item)-[:IN_CATEGORY]->(cat:Category)<-[:IN_CATEGORY]-(rec:Product) WHERE NOT EXISTS { (user)-[:RATED]->(rec) } RETURN rec, cat.name AS category, COUNT(*) AS score ORDER BY score DESC LIMIT 100 } WITH category, COLLECT(rec)[..2] AS items_per_category UNWIND items_per_category AS item RETURN item.id, item.name, category LIMIT 20; </code></pre></div> <h4 id="serendipitous-recommendations" class="position-relative d-flex align-items-center group"> Serendipitous 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="serendipitous-recommendations" aria-haspopup="dialog" aria-label="Share link: Serendipitous Recommendations"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Include some unexpected but potentially interesting items MATCH (user:User {id: $user_id})-[:PURCHASED]->(item:Product) WITH user, COLLECT(DISTINCT item.category) AS user_categories MATCH (unexpected:Product) WHERE NOT unexpected.category IN user_categories AND NOT EXISTS { (user)-[:PURCHASED|VIEWED]->(unexpected) } AND unexpected.rating >= 4.0 WITH unexpected, RAND() AS randomness, unexpected.rating AS quality RETURN unexpected.id, unexpected.name, unexpected.category, (quality * 0.7 + randomness * 0.3) AS discovery_score ORDER BY discovery_score DESC LIMIT 5; </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="materialized-similarity" class="position-relative d-flex align-items-center group"> Materialized 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="materialized-similarity" aria-haspopup="dialog" aria-label="Share link: Materialized Similarity"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Pre-compute item similarities MATCH (item1:Product)<-[:PURCHASED]-(user:User)-[:PURCHASED]->(item2:Product) WHERE ID(item1) < ID(item2) WITH item1, item2, COUNT(DISTINCT user) AS co_purchases WHERE co_purchases >= 5 MERGE (item1)-[sim:SIMILAR_TO]-(item2) SET sim.score = co_purchases, sim.updated = CURRENT_TIMESTAMP; -- Use pre-computed similarities MATCH (user:User {id: $user_id})-[:PURCHASED]->(item:Product)-[sim:SIMILAR_TO]-(rec:Product) WHERE NOT EXISTS { (user)-[:PURCHASED]->(rec) } RETURN rec.id, rec.name, MAX(sim.score) AS similarity ORDER BY similarity DESC LIMIT 20; </code></pre></div> <h4 id="caching-user-profiles" class="position-relative d-flex align-items-center group"> Caching User Profiles <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-user-profiles" aria-haspopup="dialog" aria-label="Share link: Caching User Profiles"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Cache user preference profiles MATCH (user:User {id: $user_id})-[r:RATED]->(item:Product)-[:HAS_TAG]->(tag:Tag) WHERE r.rating >= 4 WITH user, tag, COUNT(*) AS tag_frequency MERGE (user)-[pref:PREFERS]->(tag) SET pref.score = tag_frequency, pref.updated = CURRENT_TIMESTAMP; -- Use cached profiles for fast recommendations MATCH (user:User {id: $user_id})-[pref:PREFERS]->(tag:Tag)<-[:HAS_TAG]-(rec:Product) WHERE NOT EXISTS { (user)-[:RATED]->(rec) } RETURN rec.id, rec.name, SUM(pref.score) AS match_score ORDER BY match_score DESC LIMIT 20; </code></pre></div> <h3 id="best-practices" class="position-relative d-flex align-items-center group"> Best Practices <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="best-practices" aria-haspopup="dialog" aria-label="Share link: Best Practices"> Share link </button> </h3> <h4 id="model-design" class="position-relative d-flex align-items-center group"> Model Design <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="model-design" aria-haspopup="dialog" aria-label="Share link: Model Design"> Share link </button> </h4>Explicit vs Implicit Feedback: Track both explicit ratings and implicit signals (views, clicks, time spent). Temporal Decay: Weight recent interactions more heavily than old ones. Negative Signals: Consider dislikes, skips, and returns as negative feedback. Cold Start: Have fallback strategies for new users (popular items) and new items (content-based). <h4 id="quality-metrics" class="position-relative d-flex align-items-center group"> Quality 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="quality-metrics" aria-haspopup="dialog" aria-label="Share link: Quality Metrics"> Share link </button> </h4>Precision: Measure how many recommendations were actually relevant. Coverage: Ensure recommendations span the full catalog, not just popular items. Diversity: Track category and attribute diversity in recommendation sets. Serendipity: Measure user engagement with unexpected recommendations. <h3 id="integration-examples" class="position-relative d-flex align-items-center group"> Integration Examples <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="integration-examples" aria-haspopup="dialog" aria-label="Share link: Integration Examples"> Share link </button> </h3> <h4 id="python-client" class="position-relative d-flex align-items-center group"> Python Client <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="python-client" aria-haspopup="dialog" aria-label="Share link: Python Client"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">from geode_client import Client async def get_recommendations(client, user_id, limit=20): # Collaborative filtering recommendations result, _ = await client.query(""" MATCH (user:User {id: $user_id})-[:RATED]->(item:Product)<-[:RATED]-(similar:User) WHERE user <> similar WITH similar, COUNT(DISTINCT item) AS common_items WHERE common_items >= 3 MATCH (similar)-[r:RATED]->(rec:Product) WHERE NOT EXISTS { (user)-[:RATED]->(rec) } AND r.rating >= 4 RETURN rec.id AS product_id, rec.name AS product_name, COUNT(DISTINCT similar) AS score, AVG(r.rating) AS avg_rating ORDER BY score DESC, avg_rating DESC LIMIT $limit """, {'user_id': user_id, 'limit': limit}) return [ { 'product_id': row[0], 'name': row[1], 'score': row[2], 'rating': row[3] } for row in result.rows ] </code></pre></div> <h4 id="rust-client" class="position-relative d-flex align-items-center group"> Rust Client <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="rust-client" aria-haspopup="dialog" aria-label="Share link: Rust Client"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-rust" data-lang="rust">use geode_client::Client; async fn recommend_items(client: &Client, user_id: &str, limit: i32) -> Result<Vec<Recommendation>> { let results = client.execute( "MATCH (user:User {id: $user_id})-[:LIKED]->(item:Item) \ MATCH (item)<-[:LIKED]-(other:User)-[:LIKED]->(rec:Item) \ WHERE NOT EXISTS { (user)-[:LIKED]->(rec) } \ RETURN rec.id, rec.title, COUNT(*) AS score \ ORDER BY score DESC LIMIT $limit", &[("user_id", user_id.into()), ("limit", limit.into())] ).await?; Ok(results.into_iter() .map(|row| Recommendation { item_id: row.get_string(0).unwrap(), title: row.get_string(1).unwrap(), score: row.get_int(2).unwrap(), }) .collect()) } </code></pre></div> <h3 id="related-topics" class="position-relative d-flex align-items-center group"> Related Topics <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="related-topics" aria-haspopup="dialog" aria-label="Share link: Related Topics"> Share link </button> </h3><ul> <li>Collaborative Filtering: See collaborative-filtering tag for detailed filtering techniques</li> <li>Vector Search: Check vector-search for embedding-based recommendations</li> <li>PageRank: Use pagerank for personalized recommendation scoring</li> <li>Analytics: Explore analytics tag for recommendation quality metrics</li> </ul>

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