Recommendation algorithm applicable in eCommerce site and solved using neo4j graph database

Question

I am planning to implement product recommendation in my eCommerce site using neo4j graph database .

Recommendation will be based on User action on a product. Actions will be

   - Product View , 
   - Rating ,
   - Read book
   - Download book , 
   - Purchase , 
   - Add to card , 
   - Review , 
   - Share
   - Some more action applicable to our site.

The graph structure will be

User (Node )

ID
Timestamp

Product ( Node )

Name
Timestamp

Action ( Relationship between User and Product node )

Weight ( Given based on the action , eg : purchase : 10 , view : 1 etc)
Timestamp (Time at which action occurred )

Later I will add social relationship between the User nodes .

I found different recommendation methods and algorithms from my initial analysis from internet . Following are the list which is categorized based on my understanding . Some of term might be incorrect or redundant or wrong categorization ( Correct me if I am wrong ).

 - Item-Item similarity   
       - k-nearest neighbors (k-NN) algorithm
       - Pearson correlation coefficient.   
 -  User-User similarity   
 -  Matrix Factorization    
       - Singular Value Decomposition (SVD)
       - Restricted Boltzmann Machines (RBM)
       - Non-Negative Matrix Factorization ( NNMF )
 -  Latent factor analysis   
 -  Co-visitation analysis   
 -  Latent topic analysis   
 -  Cluster model   
 -  Association rule    
       - Bi-gram matrix association rule
 -  Ensembles

My problem is to identify which all methods are applicable in my eCommerce site and can be solved using neo4j graph database ( Based on the above model ).

did you get any helpful link to start up? – Sachin Vairagi Aug 31 '16 at 07:23 — Sachin Vairagi, Aug 31 '16 at 07:23

score 1 · Answer 1 · edited Apr 13 '17 at 12:50

Your question is more about data science rather than how to implement something. Then I point you to the Data Science StackExchange.

If you want to implement your recommendation engine for eCommerce I highly recommend to use GraphAware Reco. Which is Framework for creating Recommendation Engines top of Neo4j.

Here is the scaffold for basic Recommendation Engine based on GraphAware Reco - https://github.com/graphaware/recommendations-meetup

If your application is based on PHP you can use GraphAware reco4php

Recommendation algorithm applicable in eCommerce site and solved using neo4j graph database

1 Answers1