Bayesian Deep Learning Based ExplorationExploitation for Personalized Recommendations Personalized Recommendation Systems require an effective method to balance exploration and exploitation. To learn effective strategies, user and item attributes are critical data sources to capture contextual information. In this paper, we first present an approach based on Bayesian Deep Learning to learn a compact representation of user and item attributes to guide exploitation. A key novelty of the approach lies in its ability to also capture the uncertainty associated with the model output to guide exploration. We then show how to further boost exploration by incorporating model uncertainty with that of data uncertainty. Experimental results demonstrate the benefits of our approach in terms of accuracy in recommendations as well as its performance in an online setting.

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.