# Recommendation Engine With Neo4j

{{tag>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

{{ https://i.imgur.com/ntVbKMN.jpg }}

In Cypher

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

Basic initial approach. Improvements:

  - aggregate across all purchases
  - scoring / normalize
  - compute similarity metrics

### Content Filtering

{{ https://i.imgur.com/YD1eWbb.jpg }}

In Cypher

<code>
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
</code>

#### Content Filtering - Concept Hierarchy

{{ https://i.imgur.com/qZ3NPrh.jpg }}

{{ https://i.imgur.com/qKEer5c.jpg }}




## Ref

- https://www.youtube.com/watch?v=wbI5JwIFYEM
- https://bit.ly/neo4josconslides
- https://bit.ly/neo4jnotebook