Measuring spontaneous devaluations in user preferences

Komal Kapoor; Nisheeth Srivastava; Jaideep Srivastava; Paul Schrater

doi:10.1145/2487575.2487679

Measuring spontaneous devaluations in user preferences

Komal Kapoor, Nisheeth Srivastava, Jaideep Srivastava, Paul Schrater

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Scopus citations

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)
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	https://doi.org/10.1145/2487575.2487679
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
Volume	Part F128815

Other

Other	19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
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

Access

10.1145/2487575.2487679

Cite this

Kapoor, K., Srivastava, N., Srivastava, J., & Schrater, P. (2013). Measuring spontaneous devaluations in user preferences. In R. Parekh, J. He, D. S. Inderjit, P. Bradley, Y. Koren, R. Ghani, T. E. Senator, R. L. Grossman, & R. Uthurusamy (Eds.), KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1061-1069). [2487679] (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F128815). Association for Computing Machinery. https://doi.org/10.1145/2487575.2487679

Measuring spontaneous devaluations in user preferences. / Kapoor, Komal; Srivastava, Nisheeth; Srivastava, Jaideep ; Schrater, Paul.

KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ed. / Rajesh Parekh; Jingrui He; Dhillon S. Inderjit; Paul Bradley; Yehuda Koren; Rayid Ghani; Ted E. Senator; Robert L. Grossman; Ramasamy Uthurusamy. Association for Computing Machinery, 2013. p. 1061-1069 2487679 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F128815).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kapoor, K, Srivastava, N, Srivastava, J & Schrater, P 2013, Measuring spontaneous devaluations in user preferences. in R Parekh, J He, DS Inderjit, P Bradley, Y Koren, R Ghani, TE Senator, RL Grossman & R Uthurusamy (eds), KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining., 2487679, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F128815, Association for Computing Machinery, pp. 1061-1069, 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, United States, 8/11/13. https://doi.org/10.1145/2487575.2487679

Kapoor K, Srivastava N, Srivastava J , Schrater P. Measuring spontaneous devaluations in user preferences. In Parekh R, He J, Inderjit DS, Bradley P, Koren Y, Ghani R, Senator TE, Grossman RL, Uthurusamy R, editors, KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2013. p. 1061-1069. 2487679. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2487575.2487679

Kapoor, Komal ; Srivastava, Nisheeth ; Srivastava, Jaideep ; Schrater, Paul. / Measuring spontaneous devaluations in user preferences. KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. editor / Rajesh Parekh ; Jingrui He ; Dhillon S. Inderjit ; Paul Bradley ; Yehuda Koren ; Rayid Ghani ; Ted E. Senator ; Robert L. Grossman ; Ramasamy Uthurusamy. Association for Computing Machinery, 2013. pp. 1061-1069 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

@inproceedings{f79b85b4aba943a49852a7236846c25e,

title = "Measuring spontaneous devaluations in user preferences",

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.",

keywords = "Dynamic preferences, Recommender systems, Temporalmod- els, User behavior modeling",

author = "Komal Kapoor and Nisheeth Srivastava and Jaideep Srivastava and Paul Schrater",

note = "Publisher Copyright: Copyright {\textcopyright} 2013 ACM.; 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 ; Conference date: 11-08-2013 Through 14-08-2013",

year = "2013",

month = aug,

day = "11",

doi = "10.1145/2487575.2487679",

language = "English (US)",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "Association for Computing Machinery",

pages = "1061--1069",

editor = "Rajesh Parekh and Jingrui He and Inderjit, {Dhillon S.} and Paul Bradley and Yehuda Koren and Rayid Ghani and Senator, {Ted E.} and Grossman, {Robert L.} and Ramasamy Uthurusamy",

booktitle = "KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

}

TY - GEN

T1 - Measuring spontaneous devaluations in user preferences

AU - Kapoor, Komal

AU - Srivastava, Nisheeth

AU - Srivastava, Jaideep

AU - Schrater, Paul

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 -

Measuring spontaneous devaluations in user preferences

Abstract

Publication series

Other

Bibliographical note

Keywords

Access

Other files and links

Fingerprint

Cite this