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Inicio  /  Information  /  Vol: 11 Par: 6 (2020)  /  Artículo
ARTÍCULO
TITULO

Recommender Systems Based on Collaborative Filtering Using Review Texts?A Survey

Mehdi Srifi    
Ahmed Oussous    
Ayoub Ait Lahcen and Salma Mouline    

Resumen

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.

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