IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v33y2022i1p179-202.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Konstantin Bauman & Alexander Tuzhilin, 2022. "Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes," Information Systems Research, INFORMS, vol. 33(1), pages 179-202, March.
  • Handle: RePEc:inm:orisre:v:33:y:2022:i:1:p:179-202
    DOI: 10.1287/isre.2021.1036
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.2021.1036
    Download Restriction: no

    File URL: https://libkey.io/10.1287/isre.2021.1036?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    2. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    3. Prabuddha De & Yu (Jeffrey) Hu & Mohammad S. Rahman, 2010. "Technology Usage and Online Sales: An Empirical Study," Management Science, INFORMS, vol. 56(11), pages 1930-1945, November.
    4. Nanda Kumar & Izak Benbasat, 2006. "Research Note: The Influence of Recommendations and Consumer Reviews on Evaluations of Websites," Information Systems Research, INFORMS, vol. 17(4), pages 425-439, December.
    5. Dokyun Lee & Kartik Hosanagar, 2019. "How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment," Service Science, INFORMS, vol. 30(1), pages 239-259, March.
    6. Warut Khern-am-nuai & Karthik Kannan & Hossein Ghasemkhani, 2018. "Extrinsic versus Intrinsic Rewards for Contributing Reviews in an Online Platform," Information Systems Research, INFORMS, vol. 29(4), pages 871-892, December.
    7. Bin Gu & Jaehong Park & Prabhudev Konana, 2012. "Research Note ---The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products," Information Systems Research, INFORMS, vol. 23(1), pages 182-196, March.
    8. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    9. Erik Brynjolfsson & Yu (Jeffrey) Hu & Duncan Simester, 2011. "Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales," Management Science, INFORMS, vol. 57(8), pages 1373-1386, August.
    10. Panagiotis Adamopoulos & Anindya Ghose & Vilma Todri, 2018. "The Impact of User Personality Traits on Word of Mouth: Text-Mining Social Media Platforms," Information Systems Research, INFORMS, vol. 29(3), pages 612-640, September.
    11. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
    12. Purnawirawan, Nathalia & De Pelsmacker, Patrick & Dens, Nathalie, 2012. "Balance and Sequence in Online Reviews: How Perceived Usefulness Affects Attitudes and Intentions," Journal of Interactive Marketing, Elsevier, vol. 26(4), pages 244-255.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guo, Wenhao & Tian, Jin & Li, Minqiang, 2023. "Price-aware enhanced dynamic recommendation based on deep learning," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    2. Xiong, Yingqiu & Liu, Yezheng & Qian, Yang & Jiang, Yuanchun & Chai, Yidong & Ling, Haifeng, 2024. "Review-based recommendation under preference uncertainty: An asymmetric deep learning framework," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1044-1057.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dongwon Lee & Anandasivam Gopal & Sung-Hyuk Park, 2020. "Different but Equal? A Field Experiment on the Impact of Recommendation Systems on Mobile and Personal Computer Channels in Retail," Information Systems Research, INFORMS, vol. 31(3), pages 892-912, September.
    2. Yi, Sangyoon & Kim, Dongyeon & Ju, Jaehyeon, 2022. "Recommendation technologies and consumption diversity: An experimental study on product recommendations, consumer search, and sales diversity," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    3. Xitong Li & Jörn Grahl & Oliver Hinz, 2022. "How Do Recommender Systems Lead to Consumer Purchases? A Causal Mediation Analysis of a Field Experiment," Information Systems Research, INFORMS, vol. 33(2), pages 620-637, June.
    4. Dokyun Lee & Kartik Hosanagar, 2021. "How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?," Management Science, INFORMS, vol. 67(1), pages 524-546, January.
    5. Ina Garnefeld & Sabrina Helm & Ann-Kathrin Grötschel, 2020. "May we buy your love? psychological effects of incentives on writing likelihood and valence of online product reviews," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(4), pages 805-820, December.
    6. Qihua Liu & Xiaoyu Zhang & Liyi Zhang & Yang Zhao, 2019. "The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation," Electronic Commerce Research, Springer, vol. 19(3), pages 521-547, September.
    7. Anuj Kumar & Kartik Hosanagar, 2019. "Measuring the Value of Recommendation Links on Product Demand," Information Systems Research, INFORMS, vol. 30(3), pages 819-838, September.
    8. Bin Guo & Shasha Zhou, 2017. "What makes population perception of review helpfulness: an information processing perspective," Electronic Commerce Research, Springer, vol. 17(4), pages 585-608, December.
    9. Joan Calzada & Nestor Duch-Brown & Ricard Gil, 2021. "Do search engines increase concentration in media markets?," UB School of Economics Working Papers 2021/415, University of Barcelona School of Economics.
    10. Xuan Bi & Gediminas Adomavicius & William Li & Annie Qu, 2022. "Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1644-1660, May.
    11. Jinghui (Jove) Hou & Xiao Ma, 2022. "Space Norms for Constructing Quality Reviews on Online Consumer Review Sites," Information Systems Research, INFORMS, vol. 33(3), pages 1093-1112, September.
    12. Dokyun Lee & Kartik Hosanagar, 2019. "How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment," Service Science, INFORMS, vol. 30(1), pages 239-259, March.
    13. Guy Aridor & Duarte Goncalves & Daniel Kluver & Ruoyan Kong & Joseph Konstan, 2022. "The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens," Papers 2211.14219, arXiv.org.
    14. Meiseberg, Brinja, 2016. "The Effectiveness of E-tailers’ Communication Practices in Stimulating Sales of Niche versus Popular Products," Journal of Retailing, Elsevier, vol. 92(3), pages 319-332.
    15. Jingjing Zhang & Gediminas Adomavicius & Alok Gupta & Wolfgang Ketter, 2020. "Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework," Information Systems Research, INFORMS, vol. 31(1), pages 76-101, March.
    16. Warut Khern-am-nuai & Karthik Kannan & Hossein Ghasemkhani, 2018. "Extrinsic versus Intrinsic Rewards for Contributing Reviews in an Online Platform," Information Systems Research, INFORMS, vol. 29(4), pages 871-892, December.
    17. Tobias Kretschmer & Christian Peukert, 2020. "Video Killed the Radio Star? Online Music Videos and Recorded Music Sales," Information Systems Research, INFORMS, vol. 31(3), pages 776-800, September.
    18. Hoskins, Jake D., 2020. "The evolving role of hit and niche products in brick-and-mortar retail category assortment planning: A large-scale empirical investigation of U.S. consumer packaged goods," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    19. Lorenz Graf-Vlachy & Tarun Goyal & Yannick Ouardi & Andreas König, 2021. "Reviews Left and Right: The Link Between Reviewers’ Political Ideology and Online Review Language," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 403-417, August.
    20. Miguel Godinho de Matos & Pedro Ferreira, 2020. "The Effect of Binge-Watching on the Subscription of Video on Demand: Results from Randomized Experiments," Information Systems Research, INFORMS, vol. 31(4), pages 1337-1360, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orisre:v:33:y:2022:i:1:p:179-202. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.