Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework

Microtexts are a valuable, albeit noisy, source to infer collaborative information. As music plays an important role in many human lives, microblogs on music-related activities are available in abundance. This paper investigates different strategies to estimate music similarity from these data sources. In particular, we first present a framework to extract co-occurrence scores between music artists from microblogs and then investigate 12 similarity estimation functions to subsequently derive resemblance scores. We evaluate the approaches on a collection of microblogs crawled from Twitter over a period of 10 months and compare them to standard tf - idf approaches. As evaluation criteria we use precision and recall in an artist retrieval task as well as rank proximity . We show that collaborative chatter on music can be effectively used to develop music artist similarity measures , which are a core part of every music retrieval and recommendation system. Furthermore, we analyze the effects of the “long tail” on retrieval results and investigate whether results are consistent over time, using a second dataset.