Summary of Emotion-aware Personalized Music Recommendation with a Heterogeneity-aware Deep Bayesian Network, by Erkang Jing et al.
Emotion-aware Personalized Music Recommendation with a Heterogeneity-aware Deep Bayesian Network
by Erkang Jing, Yezheng Liu, Yidong Chai, Shuo Yu, Longshun Liu, Yuanchun Jiang, Yang Wang
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed heterogeneity-aware deep Bayesian network (HDBN) is a novel music recommender system that accounts for four types of heterogeneity: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. This approach significantly outperforms baseline approaches on metrics such as HR, Precision, NDCG, and MRR using two datasets, EmoMusicLJ and EmoMusicLJ-small. The HDBN model consists of four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new music recommendation system that takes into account the different emotions people experience and their unique preferences for certain moods. The system is called Heterogeneity-aware Deep Bayesian Network (HDBN). It helps recommend songs to users based on how they’re feeling, and it’s more accurate than other systems because it considers many different factors. |
Keywords
» Artificial intelligence » Bayesian network » Neural network » Precision
