@inproceedings{8dd1b5a0bdea461cabc162bbea2d4522,
title = "Ratings vs. Reviews in Recommender Systems: A Case Study on the Amazon Movies Dataset",
abstract = "Together with the prevalence of e-commerce and online shopping, recommender systems have been playing an increasingly important role in people{\textquoteright}s daily lives in terms of discovering their potential preferences. Therein, users{\textquoteright} preferences are mostly reflected by their online behaviors, specially their evaluation towards particular items, e.g., numeric ratings and textual reviews. Many existing recommender systems focus on using item ratings to determine users{\textquoteright} preferences, while others provide approaches using textual reviews instead. In this work, via a case study on the Amazon movies data, we compare the recommendation results when using ratings or reviews, as well as that of combining both.",
keywords = "Ratings, Recommender systems, Reviews, Ratings, Recommender systems, Reviews",
author = "Maria Stratigi and Xiaozhou Li and Kostas Stefanidis and Zheying Zhang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; European Conference on Advances in Databases and Information Systems ; Conference date: 01-01-2019",
year = "2019",
doi = "10.1007/978-3-030-30278-8\_9",
language = "English",
isbn = "978-3-030-30278-8",
volume = "1064",
series = "Communications in Computer and Information Science",
publisher = "Springer-Verlag",
pages = "68--76",
editor = "Tatjana Welzer and Vili Podgorelec and \{Kami{\v s}alic Latific\}, Aida and Johann Eder and Robert Wrembel and Mikolaj Morzy and Mirjana Ivanovic and Johann Gamper and Theodoros Tzouramanis and J{\'e}r{\^o}me Darmont",
booktitle = "New Trends in Databases and Information Systems : ADBIS 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium, Bled, Slovenia, September 8-11, 2019, Proceedings",
}