Beyond Explicit and Implicit: How Users Provide Feedback to Shape Personalized Recommendation Content

要旨

As personalized recommendation algorithms become integral to social media platforms, users are increasingly aware of their ability to influence recommendation content. However, limited research has explored how users provide feedback through their behaviors and platform mechanisms to shape the recommendation content. We conducted semi-structured interviews with 34 active users of algorithmic-driven social media platforms (e.g., Xiaohongshu, Douyin). In addition to explicit and implicit feedback, this study introduced intentional implicit feedback, highlighting the actions users intentionally took to refine recommendation content through perceived feedback mechanisms. Additionally, choices of feedback behaviors were found to align with specific purposes. Explicit feedback was primarily used for feed customization, while unintentional implicit feedback was more linked to content consumption. Intentional implicit feedback was employed for multiple purposes, particularly in increasing content diversity and improving recommendation relevance. This work underscores the user intention dimension in the explicit-implicit feedback dichotomy and offers insights for designing personalized recommendation feedback that better responds to users' needs.

著者
Wenqi Li
Peking University, Beijing, China
Jui-Ching Kuo
National Tsing Hua University, Hsinchu, Taiwan
Manyu Sheng
University of Chinese Academy of Sciences, Beijing, China
Pengyi Zhang
Peking University, Beijing, China
Qunfang Wu
Harvard University, Cambridge, Massachusetts, United States
DOI

10.1145/3706598.3713241

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713241

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Recommendation and Personalization

G401
7 件の発表
2025-04-29 20:10:00
2025-04-29 21:40:00
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