AlphaBeta Sampling for Pairwise Ranking in OneClass Collaborative Filtering This paper introduces AlphaBeta Sampling (ABS) strategy, which is particularly intended for the sampling problem of pairwise ranking in oneclass collaborative filtering (PROCCF). Specifically, ABS strategy places more emphasis on such training examples, including positive item with a lower preference score and negative items with a higher preference score for each gradient step. Then, we provide the corresponding proofs for the ABS strategy from both gradient and ranking perspectives. First, we prove that sampled training examples by ABS strategy can update the model parameters with a large magnitude and analyze two instantiations by combining two specific pairwise algorithms. Second, it can be proved that ABS strategy is equivalent to optimizing for rankingaware evaluation metrics like Normalized Discounted Cumulative Gain (NDCG). Furthermore, ABS strategy can be very general and applicable in a lot of pairwise structures of pairwise algorithms. Based on ABS strategy, we provide an effective sampling algorithm to dynamically draw items for each SGD update. Finally, we evaluate the ABS strategy by conducting sampling tasks in two representative pairwise algorithms. The experiment results show that the ABS strategy performs significantly better than the baseline strategies.

Matching Novelty While Training: Novel Recommendation Based on Personalized Pairwise Loss Weighting Most works of recommender system seek to provide highly accurate item prediction while having potentially great bias to popular items. Both users and itemsu0027 providers will suffer if their system has strong preference for monotonous popular items. A better system should consider also item novelty. Previous works of novel recommendation focus mainly on reranking a topN list generated by an accuracyfocused base model. As a result, these frameworks are 2stage and essentially limited to the base model. In addition, when training the base model, the common BRP loss function treats all pairs in the same manner, consistently suppresses interesting negative items which should have been recommended. In this work, we propose a personalized pairwise novelty weighting for BPR loss function, which covers the limitations of BPR and effectively improves novelty with marginal loss in accuracy. Base model will be guided by the loss weights to learn user preference and to generate novel suggestion list in 1 stage. Comprehensive experiments on 3 public datasets show that our approach effectively promotes novelty with almost no decrease in accuracy.