Evaluation of recent advances in recommender systems on Arabic content

Journal of Big Data

Ahmed Oussous¹  Salma Mouline²  Mehdi Srifi²  Ayoub Ait Lahcen³ 

[1] Informatics Department, Faculty of Sciences and Technologies of Mohammedia (FSTM), Hassan II University;LRIT, Associated Unit to CNRST (URAC 29), Faculty of Sciences, Mohammed V University in Rabat;Laboratory of Engineering Sciences, National School of Applied Sciences (ENSA), IbnTofail University;

关键词: Arabic;    Recommender systems;    Collaborative filtering;    Rating prediction;    User reviews;    Text preprocessing;   

DOI  :  10.1186/s40537-021-00420-2

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown