Summary of Negative Feedback For Music Personalization, by M. Jeffrey Mei et al.
Negative Feedback for Music Personalization
by M. Jeffrey Mei, Oliver Bembom, Andreas F. Ehmann
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the impact of incorporating real negative feedback in training a next-song recommender system for internet radio. Traditional methods rely on randomly-sampled negative feedback, but this study demonstrates that using explicit negative samples as inputs and targets can improve test accuracy by 6% while reducing training time by 60%. Additionally, including user skips as additional inputs increases user coverage without sacrificing accuracy. The paper also explores the effect of varying numbers of random negative samples, finding that while more samples initially improve test accuracy, excessive negatives lead to false negatives and reduced performance. Furthermore, the study shows that the learned embeddings for different feedback types are robust with respect to proportion changes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how to make a music recommendation system better. Normally, these systems use fake “negative” information to help train the model. But this study shows that using real negative information can improve the system’s accuracy by 6% and make it work faster. The researchers also found that including extra information about when users skip songs helps more people get relevant recommendations without sacrificing accuracy. They also tested how well the system works with different amounts of fake negative information, finding that too much actually hurts performance. Overall, this study shows that using real negative feedback can help make music recommendation systems more accurate and user-friendly. |
