Modeling Missing Data Based on Neural Fuzzy Inference for Implicit Recommendation As implicit feedback can be tracked automatically and is easy to collect, the implicit recommendation attracts more researcher attentions. However, the uncertainty of the implicit feedback meaning poses a great challenge to the implicit recommendation. Especially for the missing data, we are not sure whether the users dislike or just have not seen the items. It may lead to bias of predictions. In this paper, we propose Neural Fuzzy Inference based on User preference and Item popularity (UINFI) algorithm to model the missing data in implicit recommendation. First, we use fuzzy set theory to represent user preference and item popularity that get from the history interactions and side information. Furthermore, neural fuzzy inference is proposed to predict the exposure possibility of missing data. Based on the fuzzy inference model, UINFI and matrix factorization model perform joint learning to predict. Experimental results show that our model has better performance compared to the other implicit recommendation algorithms.

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.