Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes

Konstantin Bauman () and Alexander Tuzhilin ()
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Konstantin Bauman: Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122
Alexander Tuzhilin: Stern School of Business, New York University, New York, New York 10012

Information Systems Research, 2022, vol. 33, issue 1, 179-202

Abstract: In this paper, we study an important problem of parsing contextual information from user reviews for recommendation purposes. First, we study the ways contextual information is expressed in user reviews and obtain novel insights about it. Among other things, we demonstrate that such type of information tends to appear at the beginning of the review, in longer sentences, in the sentences written in the past tense or using gerund form, and in the sentences referring to some points in time. Second, we propose a novel context parsing method for systematically extracting contextual information from user-generated reviews that relies on the insights obtained in our study. We apply the proposed method to three different Yelp applications (restaurants, hotels, and beauty & spas) and demonstrate that it works well and leads to better recommendation performance than the baseline approaches. Our method systematically extracts more comprehensive sets of relevant contextual variables and corresponding phrases than the baselines. Our analysis also shows the importance of the newly discovered contextual information for recommendation purposes. The obtained results and the proposed method can help to get more comprehensive knowledge about contextual variables in a given application that leads to better recommendations.

Keywords: recommender systems; contextual information; online reviews; context parsing (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:33:y:2022:i:1:p:179-202

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