用自然语言描述音乐，大模型能更准推荐。
Harnessing High-Level Song Descriptors towards Natural Language-Based Music Recommendation
- 将音乐推荐转为密集检索任务，用大模型理解用户描述
- 微调后模型在音乐类别、情绪、场景等描述上效果提升明显
- 适合做基于语义的个性化音乐推荐系统开发者参考
基于语言模型（LM）的推荐系统在帮助用户浏览大规模内容库方面日益流行。这类系统通常利用训练数据或用户偏好中的物品高层描述符（如类别或使用场景），在电影和商品等领域已被证明有效。然而，在音乐领域，语言模型如何有效利用歌曲描述符进行自然语言驱动的音乐推荐仍不明确。本文将推荐任务建模为密集检索问题，评估语言模型在逐渐熟悉任务与音乐领域数据后的表现。研究发现，随着模型在通用语言相似性、信息检索以及将长描述映射到短而高阶的音乐描述符（如流派、情绪、聆听场景）方面进行微调，其推荐性能显著提升。
原文摘要 · Abstract (English)
Recommender systems relying on Language Models (LMs) have gained popularity in assisting users to navigate large catalogs. LMs often exploit item high-level descriptors, i.e. categories or consumption contexts, from training data or user preferences. This has been proven effective in domains like movies or products. However, in the music domain, understanding how effectively LMs utilize song descriptors for natural language-based music recommendation is relatively limited. In this paper, we assess LMs effectiveness in recommending songs based on user natural language descriptions and items with descriptors like genres, moods, and listening contexts. We formulate the recommendation task as a dense retrieval problem and assess LMs as they become increasingly familiar with data pertinent to the task and domain. Our findings reveal improved performance as LMs are fine-tuned for general language similarity, information retrieval, and mapping longer descriptions to shorter, high-level descriptors in music.
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