Explicit Feedbacks Meet with Implicit Feedbacks: A Combined Approach for Recommendation System

Abstract

Recommender systems recommend items more accurately by analyzing users’ potential interest on different brands’ items. In conjunction with users’ rating similarity, the presence of users’ implicit feedbacks like clicking items, viewing items specifications, watching videos etc. have been proved to be helpful for learning users’ embedding, that helps better rating prediction of users. Most existing recommender systems focus on modeling of ratings and implicit feedbacks ignoring users’ explicit feedbacks. Explicit feedbacks can be used to validate the reliability of the particular users and can be used to learn about the users’ characteristic. Users’ characteristic mean what type of reviewers they are. In this paper, we explore three different models for recommendation with more accuracy focusing on users’ explicit feedbacks and implicit feedbacks. First one is $RHC-PMF$ that predicts users’ rating more accurately based on user’s three explicit feedbacks (rating, helpfulness score and centrality) and second one is $RV-PMF$, where user’s implicit feedback (view relationship) is considered. Last one is $RHCV-PMF$, where both type of feedbacks are considered. In this model users’ explicit feedbacks’ similarity indicate the similarity of their reliability and characteristic and implicit feedback’s similarity indicates their preference similarity. Extensive experiments on real world dataset, i.e. Amazon.com online review dataset shows that our models perform better compare to base-line models in term of users’ rating prediction. $RHCV-PMF$ model also performs better rating prediction compare to baseline models for cold start users and cold start items.