发表机构
National Technical University of Athens; National & Kapodistrian University of Athens（雅典国家技术大学; 雅典国立卡波迪斯特里安大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SAGA-CDR框架，利用CGAN和LLM实现情绪引导的书籍到音乐推荐，在Amazon和Douban数据集上取得最优评分预测精度。
AI 中文摘要
与文本情绪相匹配的背景音乐已被证明能让读者更加沉浸并改善阅读体验，这推动了将书籍与情绪匹配的音乐配对的推荐系统的发展。在此方向上，我们提出了用于跨领域推荐的情绪感知生成对抗网络（SAGA-CDR），这是一个两阶段的跨领域推荐框架，能够个性化音乐推荐并在情绪上与正在阅读的书籍对齐。在第一阶段，基于Transformer的情绪嵌入从用户评论中构建，并通过条件生成对抗网络（CGAN）跨领域映射，其掩码条件生成器处理缺失的情绪成分并注入随机性以实现更丰富的偏好迁移。随后，一个紧凑的评分神经网络将特定于情绪的交互分数与协同过滤先验融合，以预测音乐评分。在第二阶段，大型语言模型将每本书分类到效价-唤醒情绪象限中，并筛选候选曲目以匹配该象限。在英文Amazon和中文Douban数据集上的实验表明，SAGA-CDR在Amazon上取得了最佳的评分预测准确率（RMSE 0.98），在Douban上取得了最低的RMSE（0.91），其排序性能与最强的情绪感知基线相当，即使在跨语言设置下也是如此。
英文摘要
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.
Comments9 pages, 5 figures, 5 tables. Accepted at SENTIRE 2026 (ICDM 2026 Workshops)
