research-article

Bringing Friends into the Loop of Recommender Systems: An Exploratory Study

Authors:

Yong LiAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 5, Issue CSCW2

Article No.: 439, Pages 1 - 26

https://doi.org/10.1145/3479583

Published: 18 October 2021 Publication History

Abstract

The recommender system (RS), as a computer-supported information filtering system, is ubiquitous and influences what we eat, watch, or even like. In online RS, interactions between users and the system form a feedback loop: users take actions based on the recommendations provided by RS, and RS updates its recommendations accordingly. As such interactions increase, the issue of recommendation homogeneity intensifies, which significantly impairs user experience. In the face of this long-standing issue, the newly-emerging social e-commerce offers a new solution -- bringing friends' recommendations into the loop (friend-in-the-loop). In this paper, we conduct an exploratory study on the benefits of friend-in-the-loop through mixed methods on a leading social e-commerce platform in China, Beidian. We reveal that friend-in-the-loop provides users with more accurate and diverse recommendations than merely RS, and significantly alleviates algorithmic homogeneity. Moreover, our qualitative results demonstrate that the introduction of friends' external knowledge, consumers' trust, and empathy accounts for these benefits. Overall, we elaborate that friend-in-the-loop comprehensively benefits both users and RS, and it is a promising HCI-based solution to recommendation homogeneity, which offers insightful implications on designing future human-algorithm collaboration models.

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Index Terms

Bringing Friends into the Loop of Recommender Systems: An Exploratory Study
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing
  2. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 5, Issue CSCW2

CSCW2

October 2021

5376 pages

EISSN:2573-0142

DOI:10.1145/3493286

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Apple Inc., United States

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Published: 18 October 2021

Published in PACMHCI Volume 5, Issue CSCW2

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Cited By

Choi RPark SHan SLee S(2024)FoodCensor: Promoting Mindful Digital Food Content Consumption for People with Eating DisordersProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641984(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641984
Haruyama MHidaka K(2024)What influences users to provide explicit feedback? A case of food delivery recommendersUser Modeling and User-Adapted Interaction10.1007/s11257-023-09385-834:3(753-796)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11257-023-09385-8
Mysore SJasim MSong HAkbar SRandall AMahyar N(2023)How Data Scientists Review the Scholarly LiteratureProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578309(137-152)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578309
Rezaimehr FDadkhah C(2023)T&TRS: robust collaborative filtering recommender systems against attacksMultimedia Tools and Applications10.1007/s11042-023-16641-x83:11(31701-31731)Online publication date: 18-Sep-2023
https://doi.org/10.1007/s11042-023-16641-x
Chen ZCao HLan XLu ZLi Y(2022)Beyond Virtual Bazaar: How Social Commerce Promotes Inclusivity for the Traditionally Underserved Community in Chinese Developing RegionsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517487(1-15)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517487

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