TY - JOUR
T1 - Reinforcement learning-based news recommendation system
AU - Aboutorab, Hamed
AU - Hussain, Omar K.
AU - Saberi, Morteza
AU - Hussain, Farookh Khadeer
AU - Prior, Daniel
N1 - Publisher Copyright:
© 2008-2012 IEEE.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - machine learning
KW - recommender system
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85176357665&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85176357665&partnerID=8YFLogxK
U2 - 10.1109/TSC.2023.3326197
DO - 10.1109/TSC.2023.3326197
M3 - Article
AN - SCOPUS:85176357665
SN - 1939-1374
VL - 16
SP - 4493
EP - 4502
JO - IEEE Transactions on Services Computing
JF - IEEE Transactions on Services Computing
IS - 6
ER -