Post-fusion monolithic hybrid recommender system for suggesting relevant movies to users

Abstract

Recommendation systems have become the fundamental services to facilitate users information access. Generally, recommendation system works by filtering historical behaviors to understand and learn users preferences. With the growth of online information, recommendations have become of crucial importance in information filtering to prevent the information overload problem. In this study, we considered hybrid post-fusion of two approaches of collaborative filtering, by using sequences of watched movies and considering the related movies rating. After considering both techniques and applying the weights matrix, the recommendations would be modified to correspond to the users preference as needed. We discussed that various weights would be set based on use cases. For instance, in cases where we have the rating for most classes, we will assign a higher weight to the rating matrix and in case where the rating is unavailable for the majority of cases, the higher weights might be assigned to the sequential dataset. An extensive discussion is made in the context of this paper. Sequential type of the watched movies was used in conjunction of the rating as especially that model might be inadequate in distinguishing users long-term preference and that does not account for the rating of the watched movies and thus that model along might not suffice. Extensive discussion was made regarding the literature and methodological approach to solve the problem.