测量用户偏好的自发贬值

Komal Kapoor, Nisheeth Srivastava, J. Srivastava, P. Schrater
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引用次数: 15

摘要

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。
Measuring spontaneous devaluations in user preferences
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.
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