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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 37th European Conference on Information Retrieval |
| Number of pages | 6 |
| Publication status | Published - Mar 2015 |
Fields of science
- 202002 Audiovisual media
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
- 102015 Information systems
JKU Focus areas
- Computation in Informatics and Mathematics
- Engineering and Natural Sciences (in general)