Target-driven user preference transferring recommendation.

In the age of information overload, modern recommendation systems provide an important role in helping people screen massive information. With the development of deep learning, personalized recommendations are further promoted to enhance user engagement and improve user satisfaction. Meanwhile, when the content provider has high-priority content to peddle, it often just exposes it to a conspicuous location without considering different users' preferences, which may even lead to negative impressions. To tackle this problem, in this paper, we propose a target-driven user preference transferring (TDUPTrans) module that will gradually orientate the user's preferences toward the target content. It is a brand new attempt that applies the user preference transfer process on personalized recommendations so that users don't just "see" the target item but also "like" it. To be specific, we propose a greedy-strategy-based intermediate content choosing algorithm, which can recommend different intermediate contents to users with different initial interests, and eventually lead users' interests to the target content. We give a clear interpretation of how our proposed process works through a news-based case study. Moreover, extensive experiments on real-world data sets demonstrate the interpretability of the guiding process, the abilities of our proposed TDUPTrans fusing with pre-existed recommendation algorithms, and the significantly improved target recommendation success rate with this TDUPTrans module plugged in. [ABSTRACT FROM AUTHOR]