IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v17y2017i1d10.1007_s10660-016-9240-9.html
   My bibliography  Save this article

Finding users preferences from large-scale online reviews for personalized recommendation

Author

Listed:
  • Yue Ma

    (Tsinghua University)

  • Guoqing Chen

    (Tsinghua University)

  • Qiang Wei

    (Tsinghua University)

Abstract

Along with the growth of Internet and electronic commerce, online consumer reviews have become a prevalent and rich source of information for both consumers and merchants. Numerous reviews record massive consumers’ opinions on products or services, which offer valuable information about users’ preferences for various aspects of different entities. This paper proposes a novel approach to finding the user preferences from free-text online reviews, where a user-preference-based collaborative filtering approach, namely UPCF, is developed to discover important aspects to users, as well as to reflect users’ individual needs for different aspects for recommendation. Extensive experiments are conducted on the data from a real-world online review platform, with the results showing that the proposed approach outperforms other approaches in effectively predicting the overall ratings of entities to target users for personalized recommendations. It also demonstrates that the approach has an advantage in dealing with sparse data, and can provide the recommendation results with desirable understandability.

Suggested Citation

  • Yue Ma & Guoqing Chen & Qiang Wei, 2017. "Finding users preferences from large-scale online reviews for personalized recommendation," Electronic Commerce Research, Springer, vol. 17(1), pages 3-29, March.
    • Handle: RePEc:spr:elcore:v:17:y:2017:i:1:d:10.1007_s10660-016-9240-9
    DOI: 10.1007/s10660-016-9240-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-016-9240-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-016-9240-9?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
    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. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
    4. Decker, Reinhold & Trusov, Michael, 2010. "Estimating aggregate consumer preferences from online product reviews," International Journal of Research in Marketing, Elsevier, vol. 27(4), pages 293-307.
    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. Martin P. Fritze & Andreas B. Eisingerich & Martin Benkenstein, 2019. "Digital transformation and possession attachment: examining the endowment effect for consumers’ relationships with hedonic and utilitarian digital service technologies," Electronic Commerce Research, Springer, vol. 19(2), pages 311-337, June.
    2. Nan Jing & Tao Jiang & Juan Du & Vijayan Sugumaran, 2018. "Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website," Electronic Commerce Research, Springer, vol. 18(1), pages 159-179, March.
    3. Park, Jeongeun & Yang, Donguk & Kim, Ha Young, 2023. "Text mining-based four-step framework for smart speaker product improvement and sales planning," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    4. Jitendra Kumar Rout & Kim-Kwang Raymond Choo & Amiya Kumar Dash & Sambit Bakshi & Sanjay Kumar Jena & Karen L. Williams, 2018. "A model for sentiment and emotion analysis of unstructured social media text," Electronic Commerce Research, Springer, vol. 18(1), pages 181-199, March.
    5. Shugang Li & Fang Liu & Yuqi Zhang & Boyi Zhu & He Zhu & Zhaoxu Yu, 2022. "Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review," Mathematics, MDPI, vol. 10(19), pages 1-26, September.
    6. Guo Li & Na Li, 2019. "Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network," Electronic Commerce Research, Springer, vol. 19(4), pages 779-800, December.
    7. Satish Kumar & Weng Marc Lim & Nitesh Pandey & J. Christopher Westland, 2021. "20 years of Electronic Commerce Research," Electronic Commerce Research, Springer, vol. 21(1), pages 1-40, March.
    8. Qian Wang & Jijun Yu & Weiwei Deng, 2019. "An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations," Electronic Commerce Research, Springer, vol. 19(1), pages 59-79, March.
    9. Sarah Bayer & Henner Gimpel & Daniel Rau, 2021. "IoT-commerce - opportunities for customers through an affordance lens," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 27-50, March.

    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. Daniel Kaimann & Joe Cox, 2014. "The Interaction of Signals: A Fuzzy set Analysis of the Video Game Industry," Working Papers Dissertations 13, Paderborn University, Faculty of Business Administration and Economics.
    2. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    3. Akshay Kangale & S. Krishna Kumar & Mohd Arshad Naeem & Mark Williams & M. K. Tiwari, 2016. "Mining consumer reviews to generate ratings of different product attributes while producing feature-based review-summary," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(13), pages 3272-3286, October.
    4. Moon, Sangkil & Kamakura, Wagner A., 2017. "A picture is worth a thousand words: Translating product reviews into a product positioning map," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 265-285.
    5. Daniel Kaimann & Joe Cox, 2014. "The Interaction of Signals: A Fuzzy set Analysis of the Video Game Industry," Working Papers CIE 84, Paderborn University, CIE Center for International Economics.
    6. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    7. 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.
    8. Yi-Fen Chen & Shi-Han Chang, 2016. "The online framing effect: the moderating role of warning, brand familiarity, and product type," Electronic Commerce Research, Springer, vol. 16(3), pages 355-374, September.
    9. Dinesh Puranam & Vishal Narayan & Vrinda Kadiyali, 2017. "The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors," Marketing Science, INFORMS, vol. 36(5), pages 726-746, September.
    10. Weijia (Daisy) Dai & Ginger Jin & Jungmin Lee & Michael Luca, 2018. "Aggregation of consumer ratings: an application to Yelp.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 289-339, September.
    11. Shugang Li & Yuqi Zhang & Yueming Li & Zhaoxu Yu, 2021. "The user preference identification for product improvement based on online comment patch," Electronic Commerce Research, Springer, vol. 21(2), pages 423-444, June.
    12. Yili Hong & Pei-yu Chen & Lorin Hitt, 2014. "Measuring Product Type with Dynamics of Online Product Review Variances: A Theoretical Model and the Empirical Applications," Working Papers 14-03, NET Institute.
    13. Philipp Herrmann, 2014. "The impact of the variance of online consumer ratings on pricing and demand – An analytical model," Working Papers Dissertations 07, Paderborn University, Faculty of Business Administration and Economics.
    14. Kun Chen & Peng Luo & Huaiqing Wang, 2017. "Investigating transitive influences on WOM: from the product network perspective," Electronic Commerce Research, Springer, vol. 17(1), pages 149-167, March.
    15. Soumya Mukhopadhyay & V Kumar & Amalesh Sharma & Tuck Siong Chung, 2022. "Impact of review narrativity on sales in a competitive environment," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2538-2556, June.
    16. Joe Cox & Daniel Kaimann, 2013. "The Signaling Effect of Critics - Evidence from a Market for Experience Goods," Working Papers CIE 68, Paderborn University, CIE Center for International Economics.
    17. Chung-Yi Lin & Shu-Yi Liaw & Chao-Chun Chen & Mao-Yuan Pai & Yuh-Min Chen, 2017. "A computer-based approach for analyzing consumer demands in electronic word-of-mouth," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 225-242, August.
    18. Yabing Jiang & Hong Guo, 2012. "Design of Consumer Review Systems and Product Pricing," Working Papers 12-10, NET Institute.
    19. Pei-Yu Chen & Yili Hong & Ying Liu, 2018. "The Value of Multidimensional Rating Systems: Evidence from a Natural Experiment and Randomized Experiments," Management Science, INFORMS, vol. 64(10), pages 4629-4647, October.
    20. Xiao Liu & Param Vir Singh & Kannan Srinivasan, 2016. "A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing," Marketing Science, INFORMS, vol. 35(3), pages 363-388, May.

    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:spr:elcore:v:17:y:2017:i:1:d:10.1007_s10660-016-9240-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.