ICARS: An Interactive ContextAware Recommender System Contextaware recommendation has attracted significant attentions over online sites due to its smart context adaption in improving recommendation quality. However, the useru0027s instant contexts do not follow hisher regular user behaviour patterns, thus have not been well captured for advanced personalization of recommendation generation. In this work, we propose an Interactive ContextAware Recommender System (ICARS), which allows users to interact and present their needs, so the system can personalize and refine user preferences. ICARS iteratively asks a question to a user to trigger feedback in term of her recent contexts and incorporates the response to recommend items most likely satisfying hisher instant interests. Specifically, we first propose a Personalized Weighted ContextAware Matrix Factorization (PWCAMF) that enables the personalization of important contexts for each user. Then we propose two question selection strategies that exploit user preferences through feedback. We have conducted comprehensive experiments over two real datasets. The experimental results prove the effectiveness of our ICARS system compare to existing competitors.

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.