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Recently, interactive recommendation systems based on reinforcement learninghave been attended by researchers due to the consider recommendation procedureas a dynamic process and update the recommendation model based on immediateuser feedback, which is neglected in traditional methods. The existing workshave two significant drawbacks. Firstly, inefficient ranking function toproduce the Top-N recommendation list. Secondly, focusing on recommendationaccuracy and inattention to other evaluation metrics such as diversity. Thispaper proposes a deep reinforcement learning based recommendation system byutilizing Actor-Critic architecture to model dynamic users' interaction withthe recommender agent and maximize the expected long-term reward. Furthermore,we propose utilizing Spotify's ANNoy algorithm to find the most similar itemsto generated action by actor-network. After that, the Total Diversity EffectRanking algorithm is used to generate the recommendations concerning relevancyand diversity. Moreover, we apply positional encoding to computerepresentations of the user's interaction sequence without usingsequence-aligned recurrent neural networks. Extensive experiments on theMovieLens dataset demonstrate that our proposed model is able to generate adiverse while relevance recommendation list based on the user's preferences.

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