A Genre-Based Item-Item Collaborative Filtering: Facing the Cold-Start Problem

Abstract

Recommender System is a technique which is used to recommend an item or product to a user based on the user's preference'. Collaborative filtering is an approach that is vastly used in recommender systems. Item-item-based collaborative filtering is a collaborative filtering recommender system technique where the user got the recommendation based on the similarity among the item ratings. Here, we present an approach where we calculate the similarity among the items based on the genre of items. Any item may belong to more than one genre or category. Based on items propensity to a specific genre or category we propose a new item-item-based similarity metric and a little improvement in prediction method that can efficiently compute the ratings and provide more accurate recommendation compare to the state-of-art works. Our model addresses the problem of the cold start since traditional similarity model takes the user ratings into account whereas our model can calculate the similarity based on item genre or category among them. We also show the extensive simulation results based on sparsity and other recommender system evaluation techniques. We also distinguish that our result outperforms than the traditional collaborative filtering recommender systems.