D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning
This work addresses the need for more efficient and diverse recommendations in interactive systems, though it is incremental as it builds on existing deep reinforcement learning methods.
The paper tackled the problem of inefficient ranking and lack of diversity in interactive recommendation systems by proposing a deep reinforcement learning model that uses Actor-Critic architecture, ANNoy algorithm, and Total Diversity Effect Ranking to generate recommendations. The result is a system that produces diverse and relevant recommendation lists based on user preferences, as demonstrated on the MovieLens dataset.
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.