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Are You Reaching Your Audience?: Exploring Item Exposure over Consumer Segments in Recommender Systems

Published: 03 July 2018 Publication History

Abstract

Many state-of-the-art recommender systems are known to suffer from popularity bias, which means that they have a tendency to recommend items that are already popular, making those items even more popular. This results in the item catalogue being not fully utilised, which is far from ideal from the business' perspective. Issues of item exposure are actually more complex than simply overexposure of popular items. In this paper we look at the exposure of individual items to different groups of consumers, the item's audience, and address the question of whether recommender systems reach each item's potential audience. Thus, we go beyond state-of-the-art analyses that have simply addressed the extent to which items are recommended, regardless of whether they are reaching their target audience. We conduct an empirical study on the MovieLens 20M dataset showing that recommender systems do not fully utilise items' audiences, and existing sales diversity optimisers do not improve their exposure.

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Cited By

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  • (2024)Mitigating Exposure Bias in Recommender Systems—A Comparative Analysis of Discrete Choice ModelsACM Transactions on Recommender Systems10.1145/36412913:2(1-37)Online publication date: 27-Jan-2024
  • (2023)Bias characterization, assessment, and mitigation in location-based recommender systemsData Mining and Knowledge Discovery10.1007/s10618-022-00913-537:5(1885-1929)Online publication date: 14-Feb-2023
  • (2021)Exploring Exposure Bias in Recommender Systems from Causality Perspective2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C)10.1109/QRS-C55045.2021.00069(425-432)Online publication date: Dec-2021
  • Show More Cited By

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cover image ACM Conferences
UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization
July 2018
393 pages
ISBN:9781450355896
DOI:10.1145/3209219
  • General Chairs:
  • Tanja Mitrovic,
  • Jie Zhang,
  • Program Chairs:
  • Li Chen,
  • David Chin
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]

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Publication History

Published: 03 July 2018

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Author Tags

  1. consumer diversity
  2. diversity
  3. item exposure
  4. item-centric evaluation
  5. recommender systems

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UMAP '18 Paper Acceptance Rate 26 of 93 submissions, 28%;
Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

View all
  • (2024)Mitigating Exposure Bias in Recommender Systems—A Comparative Analysis of Discrete Choice ModelsACM Transactions on Recommender Systems10.1145/36412913:2(1-37)Online publication date: 27-Jan-2024
  • (2023)Bias characterization, assessment, and mitigation in location-based recommender systemsData Mining and Knowledge Discovery10.1007/s10618-022-00913-537:5(1885-1929)Online publication date: 14-Feb-2023
  • (2021)Exploring Exposure Bias in Recommender Systems from Causality Perspective2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C)10.1109/QRS-C55045.2021.00069(425-432)Online publication date: Dec-2021
  • (2020)Hands on Data and Algorithmic Bias in Recommender SystemsProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3398669(388-389)Online publication date: 7-Jul-2020
  • (2020)Beyond Optimizing for Clicks: Incorporating Editorial Values in News RecommendationProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3394864(145-153)Online publication date: 7-Jul-2020
  • (2019)The Effect of Algorithmic Bias on Recommender Systems for Massive Open Online CoursesAdvances in Information Retrieval10.1007/978-3-030-15712-8_30(457-472)Online publication date: 7-Apr-2019

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