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Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations

  • In this paper we show that the extraction of opinions from free-text reviews can improve the accuracy of movie recommendations. We present three approaches to extract movie aspects as opinion targets and use them as features for the collaborative filtering. Each of these approaches requires different amounts of manual interaction. We collected a data set of reviews with corresponding ordinal (star) ratings of several thousand movies to evaluate the different features for the collaborative filtering. We employ a state-of-the-art collaborative filtering engine for the recommendations during our evaluation and compare the performance with and without using the features representing user preferences mined from the free-text reviews provided by the users. The opinion mining based features perform significantly better than the baseline, which is based on star ratings and genre information only.

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Author:Niklas JakobGND, Stefan Hagen Weber, Mark-Christoph MüllerORCiDGND, Iryna GurevychORCiDGND
Parent Title (English):TSA '09: Proceedings of the 1st international CIKM workshop on topic-sentiment analysis for mass opinion. Hong Kong, China, 6 November 2009
Publisher:Association for Computing Machinery
Place of publication:New York
Editor:David Cheung, Il-Yeol Song, Wesley Chu, Xiaohua Hu, Jimmy Lin, Jiexun Li, Zhiyong Peng
Document Type:Conference Proceeding
Year of first Publication:2009
Date of Publication (online):2022/07/19
Publishing Institution:Leibniz-Institut für Deutsche Sprache (IDS) [Zweitveröffentlichung]
Tag:algorithms; collaborative filtering; data mining; database applications; experimentation; measurement; movie recommendation; multi-relational learning; natural language processing; opinion extraction; opinion mining; recommendation system; sentiment analysis; text analysis; user preference
GND Keyword:Algorithmus; Automatische Sprachanalyse; Benutzer; Data Mining; Datenbank; Datensatz; Empfehlung; Empfehlungssystem; Film; Kollaborative Filterung; Rezension; Textanalyse
First Page:57
Last Page:64
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Licence (German):License LogoUrheberrechtlich geschützt