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

Katja Niemann, Martin Wolpers
2013 Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13  
In order to satisfy and positively surprise its 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. Such an approach also supports 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
more » ... age 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. That is to say, an item is described by the items it is significantly often used with rather than by its users or content attributes. The approach increases the rating predictions for niche items with fewer usage data available and improves the aggregate diversity of the recommendations.
doi:10.1145/2487575.2487656 dblp:conf/kdd/NiemannW13 fatcat:plqs4lso4ngs7ap6jz4xstgjai