Recommendation System Issues, Approaches and Challenges Based on User Reviews


  • Khalid Benabbes MISC Laboratory, Faculty of Sciences, Ibn Tofail University, Kénitra, Morocco
  • Khalid Housni MISC Laboratory, Faculty of Sciences, Ibn Tofail University, Kénitra, Morocco
  • Ali El Mezouary IRF-SIC Laboratory, EST, Ibn Zohr University, Agadir, Morocco
  • Ahmed Zellou SPM Research Team, ENSIAS, Mohammed V University, Rabat, Morocco



Recommender system, Collaborative filtering, Content filtering, User reviews, Survey


With the ever-increasing volume of online information, recommender systems have been effective as a strategy to overcome information overload. They have a wide range of applications in many fields, including e-learning, e-commerce, e-government and scientific research. Recommender systems are search engines that are based on the user’s browsing history to suggest a product that expresses their interests. Being usually in the form of textual comments and ratings, such reviews are a valuable source of information about users’ perceptions. Recommender systems (RSs) apply various approaches to predict users’ interest on information, products and services among a huge amount of available items. In this paper, we will describe the recommender system, discuss ongoing research in this field, and address the challenges, limitations and the techniques adopted. This paper also discusses how review texts are interpreted to solve some of the major problems with traditional recommendation techniques. To assess the value of a recommender system, qualitative evaluation measures are discussed as well in this research. Based on a series of selected articles published between 2008 and 2020, the study allowed us to conclude that the efficiency of RSs is strongly centered on the control of information context, the operated exploration algorithm, the method, and the type of processed data in addition to the information on users’ trust.


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Author Biographies

Khalid Benabbes, MISC Laboratory, Faculty of Sciences, Ibn Tofail University, Kénitra, Morocco

Khalid Benabbes is a PhD student at the MISC Laboratory, Faculty of Sciences, Ibn Tofail University, Kénitra, Morocco. He is currently a software engineer at Hassan II Institute of Agronomy & Veterinary Medicine in Rabat. He holds an engineering degree in Computer Sciences from ENSA, Agadir. His research interests include MOOC, Recommender system, Machine Learning, and Data Science.

Khalid Housni, MISC Laboratory, Faculty of Sciences, Ibn Tofail University, Kénitra, Morocco

Khalid Housni received his PhD in computer sciences from the University of Ibn Zohr Agadir, Morocco. In 2014, he joined the Department of Computer Science, Faculty of Sciences, Ibn Tofail University, Kénitra, Morocco. His research interests include image/video processing and networks reliability.

Ali El Mezouary, IRF-SIC Laboratory, EST, Ibn Zohr University, Agadir, Morocco

Ali El Mezouary received his PhD in computer sciences from the University of Ibn Zohr, Agadir, Morocco. He is a full professor at ESTA, University of Ibn Zohr. His research interests include Web Semantic and Adaptive Learning Systems.

Ahmed Zellou, SPM Research Team, ENSIAS, Mohammed V University, Rabat, Morocco

Ahmed Zellou received his PhD in Computer Sciences from Mohammedia School of Engineering, Rabat, Morocco. He is currently a professor at the National School of Computer Science and Systems Analysis (ENSIAS). His research interests include Information Integration, Hybrid, Semantic and Fuzzy, Content Integration, Semantic P2P, Cloud Integration.


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