Reinforcement learning-based news recommendation system

Hamed Aboutorab, Omar K. Hussain, Morteza Saberi, Farookh Khadeer Hussain, Daniel Prior

Research output: Contribution to journalArticlepeer-review

Abstract

Recommender systems have seen wide adoption in different domains. The motive of such systems has evolved from providing generic recommendations in the past to providing customized and user-focused recommendations. To achieve this aim, the complexity and sophistication of the underlying techniques such systems use have evolved. Current recommender systems use advanced Artificial Intelligence techniques to provide intelligent recommendations and adapt their future workings to the user's interest and requirements. One such technique currently being used in the literature to achieve this aim is Reinforcement Learning. However, a drawback of this technique is that it is data intensive and needs to be trained on data that represent different scenarios to ensure that the recommended output in a given scenario is accurate. In this article, we present an approach, namely Reinforcement Learning-based News Recommendation System (RL-NRS), to address this drawback in the domain of news recommendation. We explain the different stages of RL-NRS in detail and compare its performance with news articles recommended by Google for a particular search term.
Original languageEnglish
Pages (from-to)4493-4502
Number of pages10
JournalIEEE Transactions on Services Computing
Volume16
Issue number6
Early online dateOct 2023
DOIs
Publication statusPublished - 01 Nov 2023

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