TY - GEN
T1 - Measuring spontaneous devaluations in user preferences
AU - Kapoor, Komal
AU - Srivastava, Nisheeth
AU - Srivastava, Jaideep
AU - Schrater, Paul
N1 - Publisher Copyright:
Copyright © 2013 ACM.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2013/8/11
Y1 - 2013/8/11
N2 - 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.
AB - 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.
KW - Dynamic preferences
KW - Recommender systems
KW - Temporalmod- els
KW - User behavior modeling
UR - http://www.scopus.com/inward/record.url?scp=84938075415&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84938075415&partnerID=8YFLogxK
U2 - 10.1145/2487575.2487679
DO - 10.1145/2487575.2487679
M3 - Conference contribution
AN - SCOPUS:84938075415
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1061
EP - 1069
BT - KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
A2 - Parekh, Rajesh
A2 - He, Jingrui
A2 - Inderjit, Dhillon S.
A2 - Bradley, Paul
A2 - Koren, Yehuda
A2 - Ghani, Rayid
A2 - Senator, Ted E.
A2 - Grossman, Robert L.
A2 - Uthurusamy, Ramasamy
PB - Association for Computing Machinery
T2 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
Y2 - 11 August 2013 through 14 August 2013
ER -