Clus-DR: Cluster-based pre-trained model for diverse recommendation generation

Abstract

Recommender Systems are a predictive model for personalized suggestions utilizing past interactions and experiences. Collaborative filtering is the most popular and successful approach. The core idea behind this approach is that the users expressing similar preferences in the past are considered similar and will continue to like similar recommendations in future. The similarity among the items between past and future references, however, affects diversity and coverage of the recommendation system. In this work, we focus on a less usual direction for recommendation systems by increasing the probability of retrieving unusual and novel items in the recommendation list, which are, or can be, also relevant to the users. Most of prevailing techniques for incorporating diversity are based on re-ranking methodology, which shrinks the domain of user's exposure to serendipitous items. To overcome this issue, we propose a methodology Clus-DR (Cluster-based Diversity Recommendation) that uses individual diversity of users and then pre-trained model for diverse recommendation generation. Instead of relying on re-ranking approach, we use different clustering techniques to have different groups of users with similar diversity. Experimental results using datasets of diverse domains indicate the effectiveness of the proposed Clus-DR methodology in diversity and coverage while maintaining acceptable level of accuracy.