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Recommender Systems Based on Collaborative Filtering Using Review Texts—A Survey
Content Provider | MDPI |
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Author | Srifi, Mehdi Oussous, Ahmed Lahcen, Ayoub Ait Mouline, Salma |
Copyright Year | 2020 |
Description | 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. |
Starting Page | 317 |
e-ISSN | 20782489 |
DOI | 10.3390/info11060317 |
Journal | Information |
Issue Number | 6 |
Volume Number | 11 |
Language | English |
Publisher | MDPI |
Publisher Date | 2020-06-12 |
Access Restriction | Open |
Subject Keyword | Information Cybernetical Science Recommender Systems Collaborative Filtering User Reviews Text Mining Opinion Mining Survey |
Content Type | Text |
Resource Type | Article |