Recommending the long tail items through personalized diversification

Abstract

Recommender systems which focus only on the improvement of recommendations’ accuracy are named “accuracy-centric”. These systems encounter some problems the major of which is their failure in recommending long tail items. Long tail items are the ones rated by a few users, thus, their rare participation in recommendations. To overcome this problem, it is necessary to provide recommendations by considering other aspects in addition to accuracy. One of these aspects is diversity in recommendations. As to different users who may prefer different levels of diversity in recommendations, here diversification of recommendations in a personalized manner is suggested in order to increase the participation of long tail items. The recommendation list is optimized based on three objectives of increasing the accuracy, personalizing the diversity, and reducing the popularity of the recommended items to meet the purpose. The defined multi-objective optimization problem is solved through the archived multi-objective simulated annealing algorithm. The evaluation of this proposed method on the Netflix datasets reveals that this method overcomes the long tail recommendation problem and diversifies the recommendations according to user needs while maintaining an acceptable level of accuracy.