Investigating the effectiveness of persuasive justification messages in fair music recommender systems for users with different personality traits
Bibliographic record
Abstract
In recent decades, music recommender systems have become increasingly popular and have attracted a lot of research attention. While there has been significant progress in algorithm design to improve the quality of recommendations for listeners, there are new research challenges arising in large scale systems which have to consider the interests of both listeners and artists. To ensure a sustainable community of artists and a diversity of genres, artists, and songs, the recommender needs to ensure that new artists have a chance to be heard and rated. So, in addition to the objective of optimizing the recommendation to the preferences and enjoyment of the listener, a large scale MRS has a “fairness” objective to provide new artists (the protected group) with an opportunity to be heard. Previous research shows that using persuasive explanations can increase user acceptance of the recommended items. We propose to use persuasive justification messages for songs of new artists to influence user acceptance and satisfaction with these recommendations. The messages are designed to implement the six popular Cialdini persuasive strategies. We explore the effects of different persuasive messages on users with different Big-5 (OCEAN) personality types in an online study (n=205). The findings show that users with different personality traits are receptive to different persuasive messages and suggest how to personalize the persuasive justifications to amplify their effect for users with different personalities. These results can guide the development of personalized/ adaptive persuasive recommendation justifications for fair music recommender systems leading to a better user satisfaction and mitigating the “rich get richer” effect in large-scale music recommender systems, ensuring diversity of content and sustainability of the community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".