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.

A Novel Method to Enhance Recommendation Systems via Leveraging Multiple Types of Implicit Feedbacks Recommendation system is one of the most widely applied information filtering techniques. In recent years, more and more studies on recommendation systems have shifted from explicit behaviors to implicit behaviors. Although a lot of existing implicit recommendation systems have been proven to have excellent performance, these implicit recommendation systems have two major issues. First, most of studies only consider to utilize one implicit behavior (e.g. click  no click) to learn user preference and help improve recommendation performance. Second, almost all studies neglect the important role of cooccurrence among different implicit behaviors. In this paper, to address the aforementioned challenges, we propose a novel implicit recommendation model Bayesian Personalized Ranking by leveraging Multiple types of Implicit Feedbacks (BPRMIF), which can distinguish useru0027s favorite degree and make full use of user behaviors. We further leverage the significant role of cooccurrence to highlight implicit behavior combinations that better reflect user preference. In addition, an effective objective function which is suitable for multiple types of implicit behaviors recommendation systems is adopted in our model. And we extend the usage of cooccurrence to a specific item. Ultimately, extensive experiments are conducted on three realworld datasets, including Retailrocket, Douban Book and Jobs datasets. And experimental results have demonstrated that our model outperforms several stateoftheart implicit recommendation systems in terms of recommendation performance on Retailrocket and Douban datasets.