skip to main content
10.1145/3336191.3371780acmconferencesArticle/Chapter ViewAbstractPublication PageswsdmConference Proceedingsconference-collections
research-article

Why Do People Buy Seemingly Irrelevant Items in Voice Product Search?: On the Relation between Product Relevance and Customer Satisfaction in eCommerce

Published:22 January 2020Publication History

ABSTRACT

One emerging benefit of voice assistants is to facilitate product search experience, allowing users to express orally which products they seek, and taking actions on retrieved results such as adding them to their cart or sending the product details to their mobile phone for further examination. Looking at users' behavior in product search, supported by a digital voice assistant, we have observed an interesting phenomenon where users purchase or engage with search results that are objectively judged irrelevant to their queries.

In this work, we analyze and characterize this phenomenon. We provide several hypotheses as to the reasons behind it, including users' personalized preferences, the product's popularity, the product's indirect relation with the query, the user's tolerance level, the query intent, and the product price. We address each hypothesis by conducting thorough data analyses and offer some insights with respect to users' purchase and engagement behavior with seemingly irrelevant results. We conclude with a discussion on how this analysis can be used to improve voice product search services.

References

  1. Qingyao Ai, Yongfeng Zhang, Keping Bi, Xu Chen, and W. Bruce Croft. 2017. Learning a Hierarchical Embedding Model for Personalized Product Search. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17). ACM, 645--654. https://doi.org/ 10.1145/3077136.3080813Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Omar Alonso and Stefano Mizzaro. 2009. Relevance criteria for e-commerce: a crowdsourcing-based experimental analysis. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. ACM, 760--761.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Eliot Brenner, Jun Zhao, Aliasgar Kutiyanawala, and Zheng Yan. 2018. Endto-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings. CoRR abs/1806.07296 (2018). arXiv:1806.07296 http://arxiv.org/abs/1806.07296Google ScholarGoogle Scholar
  4. Anjan Goswami, ChengXiang Zhai, and Prasant Mohapatra. [n.d.]. Towards Optimization of E-Commerce Search and Discovery. In The 2018 SIGIR Workshop On eCommerce.Google ScholarGoogle Scholar
  5. Mihajlo Grbovic, Vladan Radosavljevic, Nemanja Djuric, Narayan Bhamidipati, Jaikit Savla, Varun Bhagwan, and Doug Sharp. 2015. E-commerce in Your Inbox: Product Recommendations at Scale. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '15). ACM, 1809--1818. https://doi.org/10.1145/2783258.2788627Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Yinglong Wang, Jun Ma, and Mohan Kankanhalli. 2019. Attentive Long Short-Term Preference Modeling for Personalized Product Search. ACM Trans. Inf. Syst. 37, 2, Article 19 (Jan. 2019), 27 pages. https://doi.org/10.1145/3295822Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Ido Guy. 2016. Searching by Talking: Analysis of Voice Queries on Mobile Web Search. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '16). ACM, New York, NY, USA, 35--44. https://doi.org/10.1145/2911451.2911525Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Amir Ingber, Arnon Lazerson, Liane Lewin-Eytan, Alexander Libov, and Eliyahu Osherovich. 2018. The Challenges of Moving from Web to Voice in Product Search. In Proc. 1st International Workshop on Generalization in Information Retrieval (GLARE 2018).Google ScholarGoogle Scholar
  9. Shubhra Kanti Karmaker Santu, Parikshit Sondhi, and ChengXiang Zhai. 2017. On Application of Learning to Rank for E-Commerce Search. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17). ACM, 475--484. https://doi.org/10.1145/3077136.3080838Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Rohan Kumar, Mohit Kumar, Neil Shah, and Christos Faloutsos. 2018. Did We Get It Right? Predicting Query Performance in E-commerce Search. In SIGIR 2018 Workshop on eCommerce (ECOM18).Google ScholarGoogle Scholar
  11. Beibei Li, Anindya Ghose, and Panagiotis G. Ipeirotis. 2011. Towards a Theory Model for Product Search. In Proceedings of the 20th International Conference on World Wide Web (WWW '11). ACM, 327--336. https://doi.org/10.1145/1963405. 1963453Google ScholarGoogle Scholar
  12. Tie-Yan Liu. 2009. Learning to Rank for Information Retrieval. Found. Trends Inf. Retr. 3, 3 (March 2009), 225--331. https://doi.org/10.1561/1500000016Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Bo Long, Jiang Bian, Anlei Dong, and Yi Chang. 2012. Enhancing product search by best-selling prediction in e-commerce. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM '12). ACM, 2479--2482.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119.Google ScholarGoogle Scholar
  15. Michinari Momma, Alireza Bagheri Garakani, and Yi Sun. [n.d.]. Multi-objective Relevance Ranking. In Proceedings of ACM SIGIR Workshop on eCommerce (SIGIR 2019 eCom).Google ScholarGoogle Scholar
  16. Parikshit Sondhi, Mohit Sharma, Pranam Kolari, and ChengXiang Zhai. 2018. A Taxonomy of Queries for E-commerce Search. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR '18). ACM, 1245--1248. https://doi.org/10.1145/3209978.3210152Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Ning Su, Jiyin He, Yiqun Liu, Min Zhang, and Shaoping Ma. 2018. User Intent, Behaviour, and Perceived Satisfaction in Product Search. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM '18). ACM, 547--555. https://doi.org/10.1145/3159652.3159714Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Krysta M. Svore, Maksims N. Volkovs, and Christopher J.C. Burges. 2011. Learning to Rank with Multiple Objective Functions. In Proceedings of the 20th International Conference on World Wide Web (WWW '11). ACM, 367--376. https://doi.org/10. 1145/1963405.1963459Google ScholarGoogle Scholar
  19. Christophe Van Gysel, Maarten de Rijke, and Evangelos Kanoulas. 2018. Mix 'N Match: Integrating Text Matching and Product Substitutability Within Product Search. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM '18). ACM, New York, NY, USA, 1373--1382. https://doi.org/10.1145/3269206.3271668Google ScholarGoogle Scholar
  20. Ellen M Voorhees and Donna K. Harman. 2005. TREC: Experiment and evaluation in information retrieval. Vol. 1. MIT press Cambridge.Google ScholarGoogle Scholar
  21. Liang Wu, Diane Hu, Liangjie Hong, and Huan Liu. 2018. Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR '18). ACM, 365--374. https://doi.org/10.1145/3209978.3209993Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Qiang Wu, Christopher JC Burges, Krysta M Svore, and Jianfeng Gao. 2010. Adapting boosting for information retrieval measures. Information Retrieval 13, 3 (2010), 254--270.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Why Do People Buy Seemingly Irrelevant Items in Voice Product Search?: On the Relation between Product Relevance and Customer Satisfaction in eCommerce

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining
      January 2020
      950 pages
      ISBN:9781450368223
      DOI:10.1145/3336191

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 22 January 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate498of2,863submissions,17%

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader