Neural collaborative filtering based recommendation system for purchased product recommendation

Abstract

In order to validate that the problem exists, I followed the procedure as explained below. First I grouped the data set with user id and the product. Then, for each user and item, I have derived the number of views, transactions and add to cart events. Then, I have created 10 new data sets. For the first five data sets, I have assigned different weights based on the event type (i.e. view, purchase or transaction). As for the second five data sets, they were created with different volumes of view, transaction and purchased events. Then I have verified that, with the presence of outliers (view events), the purchased products are not recommended to the user. To verify this behaviour I have used Bayesian Personalized Ranking, Neural Collaborative Filtering, Generalized Matrix Factorization, Most Pop, Item KNN adjusted and Multi-Layer Perceptron models. Thereafter, I have removed view data from the data set and grouped data records based on the product and user. Next I have used a weighting scheme combined with binning to derive a rating score. Next, I have used four models to verify my solution. These includes, Bayesian Personalized Ranking, Neural Collaborative Filtering, Item KNN adjusted, Generalized Matrix Factorization and Multi-Layer Perceptron. I have used fivefold cross validation to train the models and used a separate data set for validation. The results were promising. I received a Hit ratio 0.275 for HR@10. This was a major improvement as, before this the Hit ratio was near to 0.