D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning

Recently, interactive recommendation systems based on reinforcement learning have been attended by researchers due to the consider recommendation procedure as a dynamic process and update the recommendation model based on immediate user feedback, which is neglected in traditional methods. The existing works have two significant drawbacks. Firstly, inefficient ranking function to produce the Top-N recommendation list. Secondly, focusing on recommendation accuracy and inattention to other evaluation metrics such as diversity. This paper proposes a deep reinforcement learning based recommendation system by utilizing Actor-Critic architecture to model dynamic users' interaction with the recommender agent and maximize the expected long-term reward. Furthermore, we propose utilizing Spotify's ANNoy algorithm to find the most similar items to generated action by actor-network. After that, the Total Diversity Effect Ranking algorithm is used to generate the recommendations concerning relevancy and diversity. Moreover, we apply positional encoding to compute representations of the user's interaction sequence without using sequence-aligned recurrent neural networks. Extensive experiments on the MovieLens dataset demonstrate that our proposed model is able to generate a diverse while relevance recommendation list based on the user's preferences.

A Hybrid Approach to Enhance Pure Collaborative Filtering based on Content Feature Relationship

Recommendation systems get expanding significance because of their applications in both the scholarly community and industry. With the development of additional data sources and methods of extracting new information other than the rating history of clients on items, hybrid recommendation algorithms, in which some methods have usually been combined to improve performance, have become pervasive. In this work, we first introduce a novel method to extract the implicit relationship between content features using a sort of well-known methods from the natural language processing domain, namely Word2Vec. In contrast to the typical use of Word2Vec, we utilize some features of items as words of sentences to produce neural feature embeddings, through which we can calculate the similarity between features. Next, we propose a novel content-based recommendation system that employs the relationship to determine vector representations for items by which the similarity between items can be computed (RELFsim). Our evaluation results demonstrate that it can predict the preference a user would have for a set of items as good as pure collaborative filtering. This content-based algorithm is also embedded in a pure item-based collaborative filtering algorithm to deal with the cold-start problem and enhance its accuracy. Our experiments on a benchmark movie dataset corroborate that the proposed approach improves the accuracy of the system.