Music Recommendation

Latest papers

15 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

Reading the Mood: Emotion-Guided Book-to-Music Recommendation via CGANs and LLMs
Oct 5, 2026Manousos Linardakis, Georgios AlexandridisCross-Domain Recommendation Recommender Systems
School of Electrical & Computer Engineering National Technical University of Athens Zografou, 15780, Greece · Department of Digital Industry Technologies National & Kapodistrian University of Athens Evripos Complex, Psachna, 34400, Greece
Textual User Taste: Natural-Language User Context for Foundation-Model Recommender System at Scale
Sep 28, 2026Ghazal Fazelnia, Paul Gigioli, Eliza Klyce +16Recommender Systems Music Recommendation
Spotify, USA, UK, Sweden
Overview and Analysis of the RecSys Challenge 2026: Conversational Music Recommendation
Sep 27, 2026Seungheon Doh, Sergio Oramas, Bruno Sguerra +3Recommender Systems Music Recommendation
Sony Group Corporation Japan · SiriusXM United States · Deezer Research France +3
Nearest but Not Dearest: Shared Curator-Feedback Infrastructure for Content-Only Search and Recommendation
Sep 24, 2026Matt SandlerRecommender Systems Music Information Retrieval
Feed.fm San Francisco, California, USA
Explainable Recommendations at Scale: LLM Rationales for YouTube Music Artist Discovery
Sep 20, 2026Xiao Liu, Yanwei Song, Srivaths Ranganathan +7Music Recommendation Recommender Systems
Google LLC USA
Recovering Expert Critic-Sourced Network Adjacency between Musical Artists from Acoustic Distributions: A Construct-Validity Approach
Aug 27, 2026Elena Badillo-Goicoechea, Fengfeng HeCold-Start Recommendation Link Prediction
University of Chicago Chicago, Illinois, USA
Multi-Modal Semantic Expansion with Constrained LLM Reranking for Conversational Music Recommendation
Aug 24, 2026Naman Garg, Sarika Jain, George FazekasMusic Recommendation Multimodal Recommendation
National Institute of Technology Kurukshetra India · Queen Mary University of London United Kingdom
Two Views, One Voice: Evidence-Grounded Conversational Music Recommendation
Jul 24, 2026Sungwook Yoo, Sewook YooRecommender Systems Music Recommendation
Samsung South Korea
Multimodal Video-to-Music Recommendation via Semantic Retrieval and Temporal Reranking
Jul 7, 2026Seungheon Doh, Minhee Lee, Sangmoon Lee +2Cross-Modal Alignment Multimodal Embedding
Graduate School of Culture Technology, KAIST, South Korea · Gaudio Lab, Inc, South Korea
Music Playlist Captioning at Scale with Large Language Models
Jun 21, 2026Mathieu Delcluze, Léa Briand, Benjamin Chapus +2Music Recommendation Text Generation
Deezer Research · SJTU Paris Elite Institute of Technology
Multimodal Music Recommendation System using LLMs
May 28, 2026Srikar Prabhas Kandagatla, Sreehitha R. Narayana, Chandana Magapu +6Sequential Recommendation Recommender Systems
University of Massachusetts Amherst, Massachusetts, USA · Dolby Laboratories Atlanta, Georgia, USA · Adobe Research San Jose, California, USA +2
Affective Music Recommendation: A Rollout-Based World Model for Offline Preference Optimization
May 27, 2026Audrey Chan, Aaron Labbé, Jacob Lavoie +4Valence-Arousal Modeling Recommender Systems
LUCID Inc. · Toronto, Canada · Montréal, Canada +1
Reddit2Deezer: A Scalable Dataset for Real-World Grounded Conversational Music Recommendation
May 9, 2026Haven Kim, Julian McAuleyRecommender Systems Music Recommendation
University of California San Diego
MeloTune: On-Device Arousal Learning and Peer-to-Peer Mood Coupling for Proactive Music Curation
Apr 12, 2026Hongwei XuMusic Recommendation Recommender Systems
DialBGM: A Benchmark for Background Music Recommendation from Everyday Multi-Turn Dialogues
Apr 9, 2026Joonhyeok Shin, Jaehoon Kang, Yujun Lee +4Music Recommendation Recommender Systems
Sungkyunkwan University, Republic of Korea

Oct 5, 2026J/K move · Enter open · S save

Reading the Mood: Emotion-Guided Book-to-Music Recommendation via CGANs and LLMs

Manousos Linardakis, Georgios Alexandridis

School of Electrical & Computer Engineering National Technical University of Athens Zografou, 15780, Greece · Department of Digital Industry Technologies National & Kapodistrian University of Athens Evripos Complex, Psachna, 34400, Greece

Cross-Domain Recommendation Recommender Systems User Preference Modeling Generative Adversarial Networks Music Recommendation

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Background music that matches the mood of a text has been shown to make readers feel more immersed and improve their reading experience, motivating recommender systems that pair books with mood-matched music. In this direction, we present Sentiment Aware Generative Adversarial Network for Cross Domain Recommendation (SAGA-CDR), a two-phase cross-domain recommendation framework that personalizes music suggestions and emotionally aligns them with the book being read. In the first phase, transformer-based sentiment embeddings are constructed from user reviews and mapped across domains via a Conditional Generative Adversarial Network, whose mask-conditioned generator handles missing sentiment components and injects stochasticity for richer preference transfer. A compact rating neural network then fuses sentiment-specific interaction scores with a collaborative filtering prior to predict music ratings. In the second phase, large language models classify each book into a valence-arousal emotional quadrant, and candidate tracks are filtered to match that quadrant. Experiments on both the English Amazon and Chinese Douban datasets show that SAGA-CDR achieves the best rating prediction accuracy on Amazon (RMSE 0.98) and the lowest RMSE on Douban (0.91), with ranking performance competitive with the strongest sentiment-aware baseline, even in cross-lingual settings.