Balancing accuracy and diversity in recommendations using matrix completion framework

Unified framework for joint accuracy-diversity optimization in recommender systems.Convex formulation utilizing item metadata for accuracy-diversity trade-off.Design an efficient algorithm using split Bregman technique for our formulation. 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-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.