Measuring spontaneous devaluations in user preferences

Komal Kapoor, Nisheeth Srivastava, J. Srivastava, P. Schrater
{"title":"Measuring spontaneous devaluations in user preferences","authors":"Komal Kapoor, Nisheeth Srivastava, J. Srivastava, P. Schrater","doi":"10.1145/2487575.2487679","DOIUrl":null,"url":null,"abstract":"Spontaneous devaluation in preferences is ubiquitous, where yesterday's hit is today's affliction. Despite technological advances facilitating access to a wide range of media commodities, finding engaging content is a major enterprise with few principled solutions. Systems tracking spontaneous devaluation in user preferences can allow prediction of the onset 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 spontaneous 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.","PeriodicalId":20472,"journal":{"name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2487575.2487679","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15

Abstract

Spontaneous devaluation in preferences is ubiquitous, where yesterday's hit is today's affliction. Despite technological advances facilitating access to a wide range of media commodities, finding engaging content is a major enterprise with few principled solutions. Systems tracking spontaneous devaluation in user preferences can allow prediction of the onset 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 spontaneous 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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
测量用户偏好的自发贬值
偏好的自发贬值无处不在,昨天的打击就是今天的痛苦。尽管技术进步促进了对广泛媒体商品的访问,但寻找吸引人的内容是一项主要的企业,几乎没有原则性的解决方案。跟踪用户偏好自发贬值的系统可以预测用户可能会对其变化的需求产生厌烦。在本研究中,我们研究了Last的音乐聆听史。FM用户关注的是他们在不同时间点对不同艺人的选择所带来的喜好变化。危险函数通常用于生存分析的统计,用于捕获用户返回艺术家的比率,作为与艺术家接触的函数。该分析提供了首个证据,证明音乐听众的偏好会自发降低。更好地理解这种现象的时间动态可以为推荐系统的相似性-多样性困境提供解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A general bootstrap performance diagnostic Flexible and robust co-regularized multi-domain graph clustering Beyond myopic inference in big data pipelines Constrained stochastic gradient descent for large-scale least squares problem Inferring distant-time location in low-sampling-rate trajectories
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1