[成果] 推荐系统和用户的协同演化

来源:作者:曾安 发布时间:2015-07-20 浏览次数:543

An Zeng, Chi Ho Yeung, Matus Medo, Yi-Cheng Zhang, Modeling mutual feedback between users and recommender systems. [Journal of Statistical Mechanics, P07020 (2015) ]

 

内容简介:我们在各种网站上常常接触并使用推荐系统。在我们享受它带来的便捷时,它也在潜移默化的影响着我们的选择习惯。前人对推荐系统的研究主要集中在如何提高推荐系统的精确性和多样性,而不同推荐系统在长期使用下会对商品形成如何的关注度分布尚不清楚。本文提出了一个基于用户-商品二分网的演化模型来研究推荐系统对用户选择行为的长期影响。虽然大家广泛认为推荐系统能为用户拓宽视野,结果显示在长期演化中用户的选择范围被推荐系统大大缩小。另外,本文还发现此模型能涌现出迟滞现象,使得推荐系统的这种不利效果很难通过自身恢复。最终,我们发现在演化早期牺牲推荐系统的少量推荐精度可以有效减弱它产生的这种长期不利影响。

 

摘要:

Recommender systems daily influence our decisions on the Internet. While considerable attention has been given to issues such as recommendation accuracy and user privacy, the long-term mutual feedback between a recommender system and the decisions of its users has been neglected so far. We propose here a model of network evolution which allows us to study the complex dynamics induced by this feedback, including the hysteresis effect which is typical for systems with non-linear dynamics. Despite the popular belief that recommendation helps users to discover new things, we find that the long-term use of recommendation can contribute to the rise of extremely popular items and thus ultimately narrow the user choice. These results are supported by measurements of the time evolution of item popularity inequality in real systems. We show that this adverse effect of recommendation can be tamed by sacrificing part of short-term recommendation accuracy.

 

原文链接:http://iopscience.iop.org/1742-5468/2015/7/P07020/