2021/08/03 by Giovanni Gabbolini, Gabbolini, Giovanni, Derek Bridge +1
Computer Science · Mathematics · #Advanced Text Analysis Techniques #Artificial intelligence #Computer science #Data mining #FOS: Computer and information sciences #Graph #Image (mathematics) #Information Retrieval (cs.IR) #Interpretability #Mathematics #Measure (data warehouse) #Music and Audio Processing #Natural language processing #Path (computing) #Pattern recognition (psychology) #Similarity (geometry) #Similarity measure #Theoretical computer science #Topic Modeling #cs.IR
paper · pdf · doi:10.48550/arxiv.2108.01632
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/08/03 · arxiv created 2021/08/04 · arxiv updated 2021/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.