基于路径相似度的可解释音乐相似性度量
An Interpretable Music Similarity Measure Based on Path Interestingness
摘要：
我们介绍了一种新的和可解释的基于路径的音乐相似性度量。我们的相似性度量假设项目，如歌曲和艺术家，以及关于这些项目的信息在知识图谱中表示。我们在图中找到种子和目标项目之间的路径;我们根据它们的兴趣度对这些路径进行评分;然后我们汇总这些分数以确定种子和目标之间的相似性。我们的相似性度量的一个显着特点是它的可解释性。特别地，我们可以将最有趣的路径翻译成自然语言，使得人类可以容易地理解相似性判断的原因。我们比较我们的相似性度量的准确性与其他竞争性的基于路径的相似性基线在两个实验设置和四个数据集。结果突出了我们的方法的有效性，音乐相似性，并表明路径兴趣分数可以是一个准确的和可解释的相似性度量的基础。
英文摘要：
We introduce a novel and interpretable path-based music similarity measure. Our similarity measure assumes that items, such as songs and artists, and information about those items are represented in a knowledge graph. We find paths in the graph between a seed and a target item; we score those paths based on their interestingness; and we aggregate those scores to determine the similarity between the seed and the target. A distinguishing feature of our similarity measure is its interpretability. In particular, we can translate the most interesting paths into natural language, so that the causes of the similarity judgements can be readily understood by humans. We compare the accuracy of our similarity measure with other competitive path-based similarity baselines in two experimental settings and with four datasets. The results highlight the validity of our approach to music similarity, and demonstrate that path interestingness scores can be the basis of an accurate and interpretable similarity measure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
