You have the choice: the Borda voting rule for clustering recommendations

Automatic recommendations are very popular in E-commerce, online shopping platforms, video on-demand services, or music-streaming. However, recommender systems often suggest too many related items such that users are unable to cope with the huge amount of recommendations. In order to avoid losing the overview in recommendations, clustering algorithms like k-means are a very common approach to manage large and confusing sets of items. In this paper, we present a clustering technique, which exploits the Borda social choice voting rule for clustering recommendations in order to produce comprehensible results for a user. Our comprehensive benchmark evaluation and experiments regarding quality indicators show that our approach is competitive to k-means and confirms the high quality of our Borda clustering approach