A new collaborative filtering approach for increasing the aggregate diversity of recommender systems

Abstract

In order to satisfy and positively surprise the users, a recommender system needs to recommend items the users will like and most probably would not have found on their own. This requires the recommender system to recommend a broader range of items including niche items as well. Such an approach also support online-stores that often offer more items than traditional stores and need recommender systems to enable users to find the not so popular items as well. However, popular items that hold a lot of usage data are more easy to recommend and, thus, niche items are often excluded from the recommendations. In this paper, we propose a new collaborative filtering approach that is based on the items' usage contexts. The approach increases the rating predictions for niche items with fewer usage data available and improves the aggragate diversity of the recommendations.