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Content-based Music Recommendation: Evolution, State of the Art, and Challenges
arXiv - CS - Information Retrieval Pub Date : 2021-07-25 , DOI: arxiv-2107.11803
Yashar Deldjoo, Markus Schedl, Peter Knees

The music domain is among the most important ones for adopting recommender systems technology. In contrast to most other recommendation domains, which predominantly rely on collaborative filtering (CF) techniques, music recommenders have traditionally embraced content-based (CB) approaches. In the past years, music recommendation models that leverage collaborative and content data -- which we refer to as content-driven models -- have been replacing pure CF or CB models. In this survey, we review 47 articles on content-driven music recommendation. Based on a thorough literature analysis, we first propose an onion model comprising five layers, each of which corresponds to a category of music content we identified: signal, embedded metadata, expert-generated content, user-generated content, and derivative content. We provide a detailed characterization of each category along several dimensions. Second, we identify six overarching challenges, according to which we organize our main discussion: increasing recommendation diversity and novelty, providing transparency and explanations, accomplishing context-awareness, recommending sequences of music, improving scalability and efficiency, and alleviating cold start. Each article addressing one or more of these challenges is categorized according to the content layers of our onion model, the article's goal(s), and main methodological choices. Furthermore, articles are discussed in temporal order to shed light on the evolution of content-driven music recommendation strategies. Finally, we provide our personal selection of the persisting grand challenges, which are still waiting to be solved in future research endeavors.

中文翻译:

基于内容的音乐推荐:进化、现状和挑战

音乐领域是采用推荐系统技术的最重要领域之一。与主要依赖协同过滤 (CF) 技术的大多数其他推荐领域相比,音乐推荐器传统上采用基于内容 (CB) 的方法。在过去的几年里,利用协作和内容数据的音乐推荐模型——我们称之为内容驱动模型——已经取代了纯 CF 或 CB 模型。在本次调查中,我们回顾了 47 篇关于内容驱动音乐推荐的文章。基于彻底的文献分析,我们首先提出了一个包含五层的洋葱模型,每一层对应于我们确定的一类音乐内容:信号、嵌入的元数据、专家生成的内容、用户生成的内容和衍生内容。我们在几个维度上提供了每个类别的详细特征。其次,我们确定了六个总体挑战,根据这些挑战,我们组织了我们的主要讨论:增加推荐多样性和新颖性,提供透明度和解释,实现上下文感知,推荐音乐序列,提高可扩展性和效率,以及缓解冷启动。每篇解决一个或多个这些挑战的文章都根据我们的洋葱模型的内容层、文章的目标和主要方法选择进行分类。此外,文章按时间顺序讨论,以阐明内容驱动的音乐推荐策略的演变。最后,我们提供了我们个人对持续存在的重大挑战的选择,
更新日期:2021-07-27
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