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.

翻译：依赖语言模型(LM)的推荐系统在协助用户浏览大型目录方面日益普及。语言模型通常利用训练数据或用户偏好中的项目高层级描述符(如类别或消费情境)。这在电影或产品等领域已被证明是有效的。然而，在音乐领域，关于语言模型如何有效利用歌曲描述符进行基于自然语言的音乐推荐，相关理解仍相对有限。本文评估了语言模型基于用户自然语言描述以及包含流派、情绪和聆听情境等描述符的项目来推荐歌曲的有效性。我们将推荐任务构建为密集检索问题，并评估语言模型随着其对任务和领域相关数据的熟悉程度增加时的表现。研究结果表明，当语言模型经过微调以处理通用语言相似性、信息检索以及将较长描述映射到音乐中较短的高层级描述符时，其性能得到了提升。