DIRS-KG: a KG-enhanced interactive recommender system based on deep reinforcement learning.

Recommender systems play a vital role in discovering contents of interest to users in this information explosion era. However, traditional recommender systems only consider user immediate feedback and tend to recommend similar items according to users' historical interactions. Moreover, in real-world online applications, they lack sufficient user interaction data. In this paper, we propose a novel interactive recommender system by using deep reinforcement learning, which can take both user immediate rewards and future rewards into account. In order to tackle the effect of interaction data insufficiency on recommendation performance, we leverage the knowledge and relation information among items in external knowledge graphs to enrich the item embedding. We concatenate the user representation and user top-l latest historical interactions as the state and feed into the Bi-LSTM model to capture user dynamic preferences. Extensive experiments on two real-world data sets demonstrate the effectiveness and generality of our proposed recommender system. [ABSTRACT FROM AUTHOR]