The 8th Workshop on Responsible Recommendation (FAccTRec 2025) was held in conjunction with the 19th ACM Conference on Recommender Systems in September, 2025 at Prague, Czech Republic, in a hybrid format.This workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairness, accountability, transparency, privacy, and other ethical and social concerns.It served to advance research and discussion of these topics in the recommender systems space, and incubate ideas for future development and refinement.For 2025, we highlight (1) the increasing importance of pre-trained models in recommendation; and (2) shifting regulatory, organizational, and political landscapes.

Music streaming services have become one of the main sources of music consumption in the last decade, with recommender systems playing a crucial role. Since these systems partially determine which songs listeners hear, they significantly influence the artists behind the music. However, when assessing the performance and fairness of music recommender systems, the perspectives of artists and others working in the music industry are often overlooked. Additionally, artists express a desire for greater transparency regarding why certain songs are recommended while others are not. This research project adopts a multi-stakeholder approach to close the gap between music recommender systems and the artists whose music they recommend. First, we gather insights from artists and music industry professionals through interviews and questionnaires. Building on those insights, we then aim to improve matching between end users and music from lesser-known artists by generating rich item and user representations. Results will be evaluated both quantitatively and qualitatively. Lastly, we plan to effectively communicate music recommender system fairness by increasing transparency for both end users and artists.

The 7th Workshop on Responsible Recommendation (FAccTRec 2024) was held in conjunction with the 18th ACM Conference on Recommender Systems on October, 2024 at Bari, Italy, in a hybrid format. This workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairness, accountability, transparency, privacy, and other ethical and social concerns. It served to advance research and discussion of these topics in the recommender systems space, and incubate ideas for future development and refinement. For 2024, the workshop highlights i) the possible tensions between the factors related to social responsibility, and ii) the challenges as a result of AI-related regulations in the European Union.

Popularity bias is a prominent phenomenon in recommender systems (RS), especially in the music domain. Although popularity bias mitigation techniques are known to enhance the fairness of RS while maintaining their high performance, there is a lack of understanding regarding users’ actual perception of the suggested music. To address this gap, we conducted a user study (n=40) exploring user satisfaction and perception of personalized music recommendations generated by algorithms that explicitly mitigate popularity bias. Specifically, we investigate item-centered and user-centered bias mitigation techniques, aiming to ensure fairness for artists or users, respectively. Results show that neither mitigation technique harms the users’ satisfaction with the recommendation lists despite promoting underrepresented items. However, the item-centered mitigation technique impacts user perception; by promoting less popular items, it reduces users’ familiarity with the items. Lower familiarity evokes discovery—the feeling that the recommendations enrich the user’s taste. We demonstrate that this can ultimately lead to higher satisfaction, highlighting the potential of less-popular recommendations to improve the user experience.

The 6th Workshop on Responsible Recommendation (FAccTRec 2023) was held in conjunction with the 17th ACM Conference on Recommender Systems on September, 2023 at Singapore, in a hybrid format. This workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairness, accountability, transparency, privacy, and other ethical and social concerns. It served to advance research and discussion of these topics in the recommender systems space, and incubate ideas for future development and refinement.

Streaming services have become one of today's main sources of music consumption, with music recommender systems (MRS) as important components. The MRS' choices strongly influence what users consume, and vice versa. Therefore, there is a growing interest in ensuring the fairness of these choices for all stakeholders involved. Firstly, for users, unfairness might result in some users receiving lower-quality recommendations in terms of accuracy and coverage. Secondly, item provider (i.e. artist) unfairness might result in some artists receiving less exposure, and therefore less revenue. However, it is challenging to improve fairness without a decrease in, for instance, overall recommendation quality or user satisfaction. Additional complications arise when balancing possibly domain-specific objectives for multiple stakeholders at once. While fairness research exists from both the user and artist perspective in the music domain, there is a lack of research directly consulting artists---with Ferraro et al. (2021) as an exception.