In e-commerce websites and related micro-blogs, users supply online reviews expressing their preferences regarding various items. Such reviews are typically in the textual comments form, and account for a valuable information source about user interests. Recently, several works have used review texts and their related rich information like review words, review topics and review sentiments, for improving the rating-based collaborative filtering recommender systems. These works vary from one another on how they exploit the review texts for deriving user interests. This paper provides a detailed survey of recent works that integrate review texts and also discusses how these review texts are exploited for addressing some main issues of standard collaborative filtering algorithms.

The education sector has never been so shaken up as much as this past year. COVID-19 has imposed new rules. Several countries were forced to switch overnight from a traditional educational model to a full eLearning one. Like most other countries, the Moroccan government decided to promote distance learning by implementing several initiatives, though they remained at an embryonic stage. To contribute to the movement of transforming the national educational landscape, we aimed to develop a solution that will leverage the technological advances in this field and influence the ways students learn. This will be possible by providing learners with the latest features enabling online and adaptive learning modes. Hence, the purpose of this first study is to provide an empirical evaluation of the existing open source Ed-tech projects, which will serve as the basis for the development of our global adaptive eLearning solution. Unlike existing work, which is based on literature reviews to compare the existing adaptive eLearning platforms, we have used the OpenBRR assessment methodology as a comparison methodology due to its flexibility and ease of use. This work will help us to understand the concepts of adaptivity in education. It will also describe the most popular open source Maturity Models as well as provide a clear idea about the differences between these Ed-tech open source solutions.

Various recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

Recommender systems are powerful tools that help users to deal with information overload problem. Collaborative Filtering (CF) approach has been widely used to build recommender systems over the past decades. However, the performance of CF is limited by sparsity and cold start problems, which are very common in real world situations. In recent years, many review-based approaches have been developed to integrate textual reviews into recommendation process, since they provide much more information about item/user profiles than ratings. The use of text analysis and opinion mining methods helps extracting such information. In this paper, we first introduce standard CF techniques and their main challenges. Then, we present different kind of information that can be extracted from user reviews. After that, we describe recent works that exploit review elements to improve the CF-based recommendations. Finally, we discuss their practical implications.

In the era of big data, recommender systems (RSs) have become growing essential tools. They represent important machine learning solutions that mainly contribute to keeping users engaged with personalized content in e-platforms. Several RSs have been proposed in the literature, and most of them have focused on English content. However, for content in other languages like Arabic, very restricted works have been done to develop RSs. In recent times, the Arabic content on the Web has increased significantly because of the growing number of Arabic web users. This highlights the need for building RSs for Arabic content. To better handle this challenge, we decided to provide the research community with a novel deep learning (DL)-based RS devoted to Arabic content. The main goal of the proposed RS is to predict user preferences from textual reviews written in the Arabic language. This is achieved by combining two independent DL techniques into one system: a convolutional neural network (CNN)-text processor for representing users and items; and a neural network, in particular, a multi-layer perceptron (MLP) to estimate interactions between user-item pairs. Extensive experiments on four large-scale Arabic datasets demonstrate that our proposed system can achieve better prediction accuracy than other state-of-the-art alternatives. Notably, it improves the MSE between 0.84% and 16.96%, and the MAE between 0.14% and 13.71%. This work is the first attempt designed to deal with a large volume of data in the Arabic context, opening up new research possibilities for future developments of Arabic RSs.