D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning release_j45cbbq7qzgftpewnvzhtofozm

by Vahid Baghi, Seyed Mohammad Seyed Motehayeri, Ali Moeini, Rooholah Abedian

Released as a article .

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.
In text/plain format

Archived Files and Locations

application/pdf  786.4 kB
file_xew27kheo5anrfxxwn4f42ubnm
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2021-10-28
Version   v1
Language   en ?
arXiv  2110.15089v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 2cb423fc-e6d3-4cf9-9a86-b8c5ca62d546
API URL: JSON