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

TL;DR

This study explores how users actively and intentionally provide feedback through various behaviors to influence personalized recommendation content on social media platforms, highlighting the importance of user intention.

Contribution

It introduces the concept of intentional implicit feedback and analyzes how different feedback types serve specific user purposes in shaping recommendations.

Findings

Explicit feedback mainly used for feed customization

Unintentional implicit feedback linked to content consumption

Intentional implicit feedback aims to increase diversity and relevance

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.