D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning
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by
Vahid Baghi, Seyed Mohammad Seyed Motehayeri, Ali Moeini, Rooholah Abedian
2021
Abstract
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.
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