Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3576840.3578278acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
short-paper
Open access

How to Make an Outlier? Studying the Effect of Presentational Features on the Outlierness of Items in Product Search Results

Published: 20 March 2023 Publication History
  • Get Citation Alerts
  • Abstract

    In two-sided marketplaces, items compete for attention from users since attention translates to revenue for suppliers. Item exposure is an indication of the amount of attention that items receive from users in a ranking. It can be influenced by factors like position bias. Recent work suggests that another phenomenon related to inter-item dependencies may also affect item exposure, viz. outlier items in the ranking. Hence, a deeper understanding of outlier items is crucial to determining an item’s exposure distribution. In this work, we study the impact of different presentational e-commerce features on users’ perception of outlierness of an item in a search result page. Informed by visual search literature, we design a set of crowdsourcing tasks where we compare the observability of three main features, viz. price, star rating, and discount tag. We find that various factors affect item outlierness, namely, visual complexity (e.g., shape, color), discriminative item features, and value range. In particular, we observe that a distinctive visual feature such as a colored discount tag can attract users’ attention much easier than a high price difference, simply because of visual characteristics that are easier to spot. Moreover, we see that the magnitude of deviations in all features affects the task complexity, such that when the similarity between outlier and non-outlier items increases, the task becomes more difficult.

    References

    [1]
    Aman Agarwal, Xuanhui Wang, Cheng Li, Michael Bendersky, and Marc Najork. 2019. Addressing Trust Bias for Unbiased Learning-to-rank. In WWW. 4–14.
    [2]
    Praveen Aggarwal and Rajiv Vaidyanathan. 2016. Is Font Size a Big Deal? A Transaction–Acquisition Utility Perspective on Comparative Price Promotions. Journal of Consumer Marketing(2016).
    [3]
    Leif Azzopardi. 2021. Cognitive Biases in Search: A Review and Reflection of Cognitive Biases in Information Retrieval. In CHIIR. ACM, 27–37.
    [4]
    Asia J Biega, Krishna P Gummadi, and Gerhard Weikum. 2018. Equity of Attention: Amortizing Individual Fairness in Rankings. In SIGIR. 405–414.
    [5]
    Fernando Diaz, Bhaskar Mitra, Michael D Ekstrand, Asia J Biega, and Ben Carterette. 2020. Evaluating Stochastic Rankings with Expected Exposure. In CIKM. 275–284.
    [6]
    John Duncan and Glyn W Humphreys. 1989. Visual Search and Stimulus Similarity. Psychological review 96, 3 (1989), 433.
    [7]
    Loann Giovannangeli, Romain Bourqui, Romain Giot, and David Auber. 2022. Color and Shape Efficiency for Outlier Detection from Automated to User Evaluation. Visual Informatics (2022).
    [8]
    Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay. 2005. Accurately Interpreting Clickthrough Data as Implicit Feedback. In SIGIR. 154–161.
    [9]
    Thorsten Joachims, Adith Swaminathan, and Tobias Schnabel. 2017. Unbiased Learning-to-rank with Biased Feedback. In WSDM. 781–789.
    [10]
    Karen C Kao, Sally Rao Hill, and Indrit Troshani. 2020. Effects of Cue Congruence and Perceived Cue Authenticity in Online Group Buying. Internet Research (2020).
    [11]
    Aniket Kittur, Ed H. Chi, and Bongwon Suh. 2008. Crowdsourcing User Studies with Mechanical Turk. In Proceedings of the 2008 Conference on Human Factors in Computing Systems, CHI. ACM, 453–456.
    [12]
    Brian McElree and Marisa Carrasco. 1999. The Temporal Dynamics of Visual Search: Evidence for Parallel Processing in Feature and Conjunction Searches. Journal of Experimental Psychology: Human Perception and Performance 25, 6(1999), 1517.
    [13]
    Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. 2018. Towards a Fair Marketplace: Counterfactual Evaluation of the Trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems. In CIKM. 2243–2251.
    [14]
    Marco Morik, Ashudeep Singh, Jessica Hong, and Thorsten Joachims. 2020. Controlling Fairness and Bias in Dynamic Learning-to-rank. In SIGIR. 429–438.
    [15]
    Alamir Novin and Eric M. Meyers. 2017. Making Sense of Conflicting Science Information: Exploring Bias in the Search Engine Result Page. In CHIIR. ACM, 175–184.
    [16]
    Zohreh Ovaisi, Ragib Ahsan, Yifan Zhang, Kathryn Vasilaky, and Elena Zheleva. 2020. Correcting for Selection Bias in Learning-to-rank Systems. In WWW. 1863–1873.
    [17]
    Piotr Sapiezynski, Wesley Zeng, Ronald E Robertson, Alan Mislove, and Christo Wilson. 2019. Quantifying the Impact of User Attentionon Fair Group Representation in Ranked Lists. In WWW. 553–562.
    [18]
    Fatemeh Sarvi, Maria Heuss, Mohammad Aliannejadi, Sebastian Schelter, and Maarten de Rijke. 2022. Understanding and Mitigating the Effect of Outliers in Fair Ranking. In WSDM. 861–869.
    [19]
    Jiye Shen, Eyal M Reingold, and Marc Pomplun. 2003. Guidance of Eye Movements during Conjunctive Visual Search: The Distractor-ratio Effect. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale 57, 2 (2003), 76.
    [20]
    Ashudeep Singh and Thorsten Joachims. 2018. Fairness of Exposure in Rankings. In KDD. 2219–2228.
    [21]
    Ashudeep Singh and Thorsten Joachims. 2019. Policy Learning for Fairness in Ranking. In NeurIPS.
    [22]
    Anne M Treisman and Garry Gelade. 1980. A Feature-integration Theory of Attention. Cognitive psychology 12, 1 (1980), 97–136.
    [23]
    Ali Vardasbi, Harrie Oosterhuis, and Maarten de Rijke. 2020. When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank. In CIKM. 1475–1484.
    [24]
    Jeremy M Wolfe. 1998. What Can 1 Million Trials Tell Us about Visual Search?Psychological Science 9, 1 (1998), 33–39.
    [25]
    Himank Yadav, Zhengxiao Du, and Thorsten Joachims. 2019. Policy-Gradient Training of Fair and Unbiased Ranking Functions. arXiv preprint arXiv:1911.08054(2019).

    Index Terms

    1. How to Make an Outlier? Studying the Effect of Presentational Features on the Outlierness of Items in Product Search Results

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        CHIIR '23: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval
        March 2023
        520 pages
        ISBN:9798400700354
        DOI:10.1145/3576840
        • Editors:
        • Jacek Gwizdka,
        • Soo Young Rieh
        This work is licensed under a Creative Commons Attribution International 4.0 License.

        Sponsors

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 20 March 2023

        Check for updates

        Author Tags

        1. Fairness
        2. Outliers
        3. Product search

        Qualifiers

        • Short-paper
        • Research
        • Refereed limited

        Conference

        CHIIR '23
        Sponsor:

        Acceptance Rates

        Overall Acceptance Rate 55 of 163 submissions, 34%

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 218
          Total Downloads
        • Downloads (Last 12 months)136
        • Downloads (Last 6 weeks)21
        Reflects downloads up to 09 Aug 2024

        Other Metrics

        Citations

        View Options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format.

        HTML Format

        Get Access

        Login options

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media