Entity representation for pairwise collaborative ranking using restricted Boltzmann machine

Pairwise preference data is one of several kinds of feedback data that many modern intelligent systems gather and process in order to help people make better decisions. Recommender systems are of main categories of such intelligent systems. An emerging recommendation approach in this domain is Pairwise Collaborative Ranking (PCR) which seeks to learn pairwise preferences of users over items. Current PCR algorithms generally learn latent factors for users and items that are consistent to the ground truth pairwise preferences, instead of directly extracting latent factors for pairwise preferences. Although this is the typical approach for rating oriented recommendation, it does not seem to be the best option for pairwise collaborative ranking. In this paper RBMRank, a novel model-based PCR approach, is introduced, that is designed for recommendation based on pairwise preference data. RBMRank uses the power of the restricted Boltzmann machines to map the entities of the system into dense feature spaces. Its embedding system is able to generate useful representations even when dealing with sparse data sets, that is a very important issue in recommender systems domain. The other important feature of RBMRank is that it is able to extract latent factors for pairwise preferences, instead of items, that allows it to directly predict the unknown preferences of each user. Experimental results on MovieLens datasets show up to 5.9% improvement of accuracy in terms of NDCG@5 and NDCG@10 compared to other state-of-the-art algorithms in the field of collaborative ranking.