Joint Heterogeneous Pair-wise Loss For Top-N Recommendation

We propose a novel pairwise unified recommendation model (short for pairwise URM). The pairwise URM combines two pairwise ranking-oriented collaborative filtering approaches, namely Collaborative Less-is-More Filtering (CLiMF) and Bayesian Personal Ranking (BPR). By sharing common latent features of users and items in BPR and CLiMF, the pairwise URM can benefit from the two methods to improve recommendation qualities. The experimental evaluation is conducted on two real-world datasets with different scales and demonstrates the positive effect of the performance of the pairwise URM.