Abstract
We investigate a range of music recommendation algorithm combinations, score aggregation functions, normalization techniques, and late fusion techniques on approximately 200 million listening events collected through Last.fm. The overall goal is to identify superior combinations for the task of artist recommendation. Hypothesizing that user characteristics influence performance on these algorithmic combinations, we consider specific user groups determined by age, gender, country, and preferred genre. Overall, we find that the performance of music recommendation algorithms highly depends on user characteristics.
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Schedl, M., Hauger, D., Farrahi, K., Tkalčič, M. (2015). On the Influence of User Characteristics on Music Recommendation Algorithms. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_37
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DOI: https://doi.org/10.1007/978-3-319-16354-3_37
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16353-6
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