Diversifying Music Recommendations
Multimedia
2018-10-04 v1 Information Retrieval
Abstract
We compare submodular and Jaccard methods to diversify Amazon Music recommendations. Submodularity significantly improves recommendation quality and user engagement. Unlike the Jaccard method, our submodular approach incorporates item relevance score within its optimization function, and produces a relevant and uniformly diverse set.
Keywords
Cite
@article{arxiv.1810.01482,
title = {Diversifying Music Recommendations},
author = {Houssam Nassif and Kemal Oral Cansizlar and Mitchell Goodman and SVN Vishwanathan},
journal= {arXiv preprint arXiv:1810.01482},
year = {2018}
}
Comments
Machine Learning for Music Discovery Workshop at the 33rd International Conference on Machine Learning (ICML'16), New York, 2016