This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Recommender systems (RS) are among the most widely used applications in data mining and machine-l... more Recommender systems (RS) are among the most widely used applications in data mining and machine-learning technologies. These technologies recommend relevant products to customers, such as movies to watch, items to buy, and books to read. The difference in user preferences over time is one of the most significant issues faced by recommender systems. Researchers have focused on time-sensitive recommender systems, and numerous studies have been conducted in this field. These studies aim to consider the time factor while offering recommendations to users by incorporating and utilizing temporal data in recommendations. In this work, we review existing works in this field and present the most prominent techniques and algorithms that have the ability to capture changes in user preferences over time, and the most important application areas for these recommendations. In addition, we present a quantitative assessment of comprehensive literature that investigates publications in terms of publication time, publication type, and datasets used. Finally, we highlight a range of findings and conclusions and provide the reader with insights based on a general analysis of time-sensitive recommender systems. INDEX TERMS Recommender systems (RSs), time sensitive recommender system, context-aware recommender systems, time aware, artificial intelligence (AI), machine learning, deep learning.
Recommender systems are used as effective information-filtering techniques to automatically predi... more Recommender systems are used as effective information-filtering techniques to automatically predict and identify sets of interesting items for users based on their preferences. Recently, there have been increasing efforts to use sentiment analysis of user reviews to improve the recommendations of recommender systems. Previous studies show the advantage of integrating sentiment analysis with recommender systems to enhance the quality of recommendations and user experience. However, limited research has been focused on recommender systems for Arabic content. This study, therefore, sets out to improve Arabic recommendation systems and investigate the impact of using sentiment analysis of user reviews on the quality of recommendations. We propose two collaborative filtering recommender systems for Arabic content: the first depends on users’ ratings, and the second uses sentiment analysis of users’ reviews to enhance the recommendations. These proposed models were tested using the Large-...
Recommender systems (RS) are among the most widely used applications in data mining and machine-l... more Recommender systems (RS) are among the most widely used applications in data mining and machine-learning technologies. These technologies recommend relevant products to customers, such as movies to watch, items to buy, and books to read. The difference in user preferences over time is one of the most significant issues faced by recommender systems. Researchers have focused on time-sensitive recommender systems, and numerous studies have been conducted in this field. These studies aim to consider the time factor while offering recommendations to users by incorporating and utilizing temporal data in recommendations. In this work, we review existing works in this field and present the most prominent techniques and algorithms that have the ability to capture changes in user preferences over time, and the most important application areas for these recommendations. In addition, we present a quantitative assessment of comprehensive literature that investigates publications in terms of publication time, publication type, and datasets used. Finally, we highlight a range of findings and conclusions and provide the reader with insights based on a general analysis of time-sensitive recommender systems. INDEX TERMS Recommender systems (RSs), time sensitive recommender system, context-aware recommender systems, time aware, artificial intelligence (AI), machine learning, deep learning.
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Papers by Nada Alshareef