Cross-Domain Recommendation

Latest papers

11 papers1 paper in the last four weeks, with none 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
FedCGR: Federated Cross-Domain Generative Recommendation
Aug 11, 2026Zhuodong Liu, Hugen Lv, Xiangyu Li +2Recommender Systems Generative Recommendation
Beijing Jiaotong University China · Shanghai Jiao Tong University China · University of Malaya Malaysia +1
TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems
Jun 23, 2026Qingyun Liu, Bo Yan, Yang Liu +15Semantic ID Recommendation Representation Learning
Google DeepMind · YouTube
Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations
Jun 9, 2026Zhuohang Jiang, Yuxin Chen, Shijie Wang +6Cross-Domain Recommendation LLM Inference Acceleration
The Hong Kong Polytechnic University Hong Kong SAR, China · Kuaishou Technology Beijing, China
Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations
Jun 4, 2026Nimesh Sinha, Raghav Saboo, Martin Wang +1Cold-Start Recommendation Cross-Domain Recommendation
DoorDash Inc. San Francisco, California, USA
Breaking the Information Silo: Semantic Personas for Cross-Domain Recommendation
Jun 1, 2026Jonathan Mayo, Moshe Unger, Konstantin BaumanCross-Domain Recommendation Cross-Domain Transfer Learning
Technology and Information Management Department, Coller School of Management, Tel Aviv University, Tel Aviv 6997801, Israel · Management Information Systems Department, Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122
Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems
May 29, 2026Xiangyu Wang, Yawen He, Shivendra Pratap Singh +12Synthetic Data Augmentation Synthetic Data Generation
Meta
HORIZON: A Benchmark for In-the-wild User Behaviour Modeling
Apr 19, 2026Arnav Goel, Pranjal A Chitale, Bhawna Paliwal +2Sequential Recommendation Cross-Domain Recommendation
Carnegie Mellon University · Microsoft Research India · University of California, Berkeley
Social Knowledge for Cross-Domain User Preference Modeling
Mar 10, 2026Nir Lotan, Adir Solomon, Ido Guy +1Cross-Domain Recommendation User Preference Modeling
University of Haifa · Ben-Gurion University of the Negev
Trinity: A Scenario-Aware Recommendation Framework for Large-Scale Cold-Start Users
Feb 28, 2026Wenhao Zheng, Wang Lu, Fangshuang Tang +4Cross-Domain Recommendation Cold-Start Recommendation
Mircosoft Corp, Redmond, WA, USA
Generative Data Transformation: From Mixed to Unified Data
Feb 26, 2026Jiaqing Zhang, Mingjia Yin, Hao Wang +6Cross-Domain Recommendation Data Augmentation
University of Science and Technology of China, Hefei, China · Beijing University of Posts and Telecommunications, Beijing, China · Huawei Noah’s Ark Lab, Shenzhen, China

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

Open paper AskPDF arXiv

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.