Table of Contents

Recommendation Engine With Neo4j
- Using Data Relationships for Recommendations
  - Collaborative Filtering
  - Content Filtering
- Ref

Recommendation Engine With Neo4j

recommendation, neo4j

Algorithm Types

Collaborative Filtering
- An algorithm that considers users interactions with products, with the assumption that other users will behave in similar ways.
Content Based Filtering
- An algorithm that considers similarities between products and categories of products

Using Data Relationships for Recommendations

Content based filtering

Recommend items based on what users have liked in the past

Collaborative filtering

Predict what users like based on the similarity of their behaviors, activities and preference to others

Collaborative Filtering

In Cypher

MATCH (will:Person {name:"Will"})-[:PURCHASED]->(b:Book)<-[:PURCHASED]-(o:Person)
MATCH (o)-[:PURHCASED]->(rec:BooK)
WHERE NOT exists((will)-[:PURCHASED]->(rec))
RETURN rec

Basic initial approach. Improvements:

aggregate across all purchases
scoring / normalize
compute similarity metrics

Content Filtering

In Cypher

MATCH (will:Person {name:"Will})-[:PURCHASED]->(b:Book)<-[:HAS_TAG]-(t:Tag)
MATCH (t)<-[:HAS_TAG]-(other:Book)
WHERE NOT exists((will)-[:PURCHASED]->(other))
RETURN other

Content Filtering - Concept Hierarchy

Ref