Frustrations with Access to Preferred Content: User Experiences with Bilibili’s Recommender Systems Reflected on Weibo

Understanding user experiences with recommender systems (RS) on audiovisual platforms is critical for user-centric design and long-term service sustainability. However, existing research often overlooks how users articulate their experiences, particularly within specific platform contexts. This research examines 38,545 Weibo posts to identify patterns in users’ expressed experiences with the RS of Bilibili (YouTube’s Chinese counterpart). Combining natural language processing and thematic analysis, we examine sentiment trends, key concerns, and underlying patterns. While RS are generally assumed to match user preferences, we found persistently expressed user frustrations with the lack of truly relevant and favored recommendations—those that align with long-term interests, highlight quality, consider context and platform-specific preferences, and respect user feedback. These frustrations are related to the platform’s business considerations and user-platform power asymmetries. The findings underscore the platform-specific nature of user experience and contribute to discussions on adaptive RS that satisfy user needs and support sustainable business models.