Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework
This work addresses the problem of balancing accuracy and diversity for users in recommender systems, which is an incremental improvement over existing methods.
The paper tackles the trade-off between accuracy and diversity in recommender systems by proposing a joint optimization model within a matrix completion framework that uses ratings and item metadata. Experimental results on a movie recommender show it achieves higher diversity for a given drop in accuracy compared to state-of-the-art techniques.
Design of recommender systems aimed at achieving high prediction accuracy is a widely researched area. However, several studies have suggested the need for diversified recommendations, with acceptable level of accuracy, to avoid monotony and improve customers experience. However, increasing diversity comes with an associated reduction in recommendation accuracy; thereby necessitating an optimum tradeoff between the two. In this work, we attempt to achieve accuracy vs diversity balance, by exploiting available ratings and item metadata, through a single, joint optimization model built over the matrix completion framework. Most existing works, unlike our formulation, propose a 2 stage model, a heuristic item ranking scheme on top of an existing collaborative filtering technique. Experimental evaluation on a movie recommender system indicates that our model achieves higher diversity for a given drop in accuracy as compared to existing state of the art techniques.