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

2025-02-14

AI Summary

This study challenges the conventional explicit/implicit feedback dichotomy by introducing the novel concept of “intentional implicit feedback”—user-initiated, strategic implicit behaviors (e.g., deliberate skipping, repeated rewatching, cross-category searching) undertaken to optimize recommendations, yet not explicitly solicited or designed by the platform. Through in-depth interviews and thematic coding with 34 active users of platforms including Xiaohongshu and Douyin, the research identifies a purpose-driven taxonomy of such feedback behaviors. Results demonstrate that intentional implicit feedback significantly enhances recommendation diversity (+37%) and perceived relevance (self-reported improvement of 2.1 points on a 5-point scale). The findings advance theoretical understanding of human–algorithm co-adaptation in recommender systems and provide empirical grounding for designing intention-aware feedback interfaces that better capture user agency and strategic engagement.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Explore user feedback mechanisms in personalized recommendations
• Introduce intentional implicit feedback for content refinement
• Align feedback behaviors with specific user purposes

Innovation

• Introduces intentional implicit feedback
• Links feedback behaviors to purposes
• Enhances content diversity and relevance