Unbiasing collaborative filtering for popularity-aware recommendation

We analyze the behavior of recommender systems relative to the popularity of the items to recommend. Our findings show that most popular ranking-based recommenders are biased towards popular items, thus affecting the quality of recommendation. Based on these observations, we propose a new deep learning architecture with an improved learning strategy that significantly improves the performance of such recommenders on low-popular items. The proposed technique is based on two main aspects: resampling of negatives and ensembling of multiple instances of the algorithm. Experimental results on traditional benchmark datasets show that the proposed approach substantially improves the recommendation ability by balancing accurate contributions almost independently from the popularity of the items to recommend.