Abstract
Spontaneous devaluation in preferences is ubiquitous, where yesterday's hit is today's affiction. Despite technological advances facilitating access to a wide range of media com- modities, finding engaging content is a major enterprise with few principled solutions. Systems tracking spontaneous devaluation in user preferences can allow prediction of the on-set of boredom in users potentially catering to their changed needs. In this work, we study the music listening histories of Last.fm users focusing on the changes in their preferences based on their choices for different artists at different points in time. A hazard function, commonly used in statistics for survival analysis, is used to capture the rate at which a user returns to an artist as a function of exposure to the artist. The analysis provides the first evidence of sponta- neous devaluation in preferences of music listeners. Better understanding of the temporal dynamics of this phenomenon can inform solutions to the similarity-diversity dilemma of recommender systems.
Original language | English (US) |
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Title of host publication | KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
Editors | Rajesh Parekh, Jingrui He, Dhillon S. Inderjit, Paul Bradley, Yehuda Koren, Rayid Ghani, Ted E. Senator, Robert L. Grossman, Ramasamy Uthurusamy |
Publisher | Association for Computing Machinery |
Pages | 1061-1069 |
Number of pages | 9 |
ISBN (Electronic) | 9781450321747 |
DOIs | |
State | Published - Aug 11 2013 |
Event | 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 - Chicago, United States Duration: Aug 11 2013 → Aug 14 2013 |
Publication series
Name | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
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Volume | Part F128815 |
Other
Other | 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 |
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Country/Territory | United States |
City | Chicago |
Period | 8/11/13 → 8/14/13 |
Bibliographical note
Publisher Copyright:Copyright © 2013 ACM.
Keywords
- Dynamic preferences
- Recommender systems
- Temporalmod- els
- User behavior modeling