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

1
LRIT, Associated Unit to CNRST (URAC 29), Mohammed V University, Rabat 10090, Morocco
2
LGS, National School of Applied Sciences (ENSA), Ibn Tofail University, Kenitra 14000, Morocco
*
Author to whom correspondence should be addressed.
Information 2020, 11(6), 317; https://doi.org/10.3390/info11060317
Received: 16 May 2020 / Revised: 7 June 2020 / Accepted: 9 June 2020 / Published: 12 June 2020
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. View Full-Text
Keywords: recommender systems; collaborative filtering; user reviews; text mining; opinion mining; survey recommender systems; collaborative filtering; user reviews; text mining; opinion mining; survey
MDPI and ACS Style

Srifi, M.; Oussous, A.; Ait Lahcen, A.; Mouline, S. Recommender Systems Based on Collaborative Filtering Using Review Texts—A Survey. Information 2020, 11, 317.

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