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We introduce a novel and interpretable path-based music similarity measure.Our similarity measure assumes that items, such as songs and artists, andinformation about those items are represented in a knowledge graph. We findpaths in the graph between a seed and a target item; we score those paths basedon their interestingness; and we aggregate those scores to determine thesimilarity between the seed and the target. A distinguishing feature of oursimilarity measure is its interpretability. In particular, we can translate themost interesting paths into natural language, so that the causes of thesimilarity judgements can be readily understood by humans. We compare theaccuracy of our similarity measure with other competitive path-based similaritybaselines in two experimental settings and with four datasets. The resultshighlight the validity of our approach to music similarity, and demonstratethat path interestingness scores can be the basis of an accurate andinterpretable similarity measure.

An Interpretable Music Similarity Measure Based on Path Interestingness