@article {Benabbes20221017,
	title = {Recommendation System Issues, Approaches and Challenges Based on User Reviews},
	journal = {Journal of Web Engineering},
	volume = {21},
	number = {4},
	year = {2022},
	note = {cited By 0},
	pages = {1017-1054},
	abstract = {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{\textquoteright}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{\textquoteright} perceptions. Recommender systems (RSs) apply various approaches to predict users{\textquoteright} 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{\textquoteright} trust. {\textcopyright} 2022 River Publishers. All rights reserved.},
	doi = {10.13052/jwe1540-9589.2143},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131073458\&doi=10.13052\%2fjwe1540-9589.2143\&partnerID=40\&md5=56b7da2c9aa6d4c44328fa667db5ef0a},
	author = {Benabbes, K. and Housni, K. and El Mezouary, A. and Zellou, A.}
}
@article {Anter20162128,
	title = {Retrieving and materializing data in hybrid mediators},
	journal = {International Journal of Applied Engineering Research},
	volume = {11},
	number = {3},
	year = {2016},
	note = {cited By 0},
	pages = {2128-2134},
	abstract = {With the emergence of the new generation of information technologies and telecommunications, the mass of information produced by individuals and enterprises has increased in a considerable manner. Thus, and in order to manage this diversity of information, the integration systems were proposed. Among these, we find the hybrid information integration systems. They allow materializing a part of data in a local database, while integrating virtually the other part. As these materialized one is organized as views, it becomes necessary to propose algorithms for this objective. Among the most interesting ones, there is k-schema. It allows organize the attributes into a set of views, while affecting an attribute to a single view. This choice causes the not loading some of data that are highly requested by users, and at the same time, it loads other that are rarely requested. In this paper, we propose a new algorithm in this end. In this latter, it is authorized to assign a same attribute to several views. We also proposed new functions for calculating the dependencies between attributes. {\textcopyright} Research India Publications.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973470278\&partnerID=40\&md5=140f0f35ca53260f11d97b5958f6ee8c},
	author = {Anter, S. and Zellou, A. and Idri, A.}
}
