将时尚周期理解为一种社会选择
Understanding Fashion Cycles as a Social Choice
摘要：
我们提出了一个研究时尚趋势的正式模型，根据时尚物品的三个参数：（1）它们的内在效用;（2）与重复使用物品相关的个人厌倦;（3）与其他人的偏好相关的社会影响。虽然有几个作品强调社会影响力在理解时尚趋势的影响，在本文中，我们将展示如何无聊在个人和社会选择中发挥重要作用。我们展示了如何无聊可以用来解释几种情况下的周期性选择，如一个人谁必须选择一家餐馆每天访问，或一个社会，必须反复“投票”的一个单一的时尚风格从集合。我们正式表明，一个社会，投票给一个单一的时尚风格可以被视为一个单一的个人通过不同的选择自行车。在我们的模型中，一件物品的效用被过去积累的无聊程度所抵消;这种无聊随着物品的每次使用而增加，当不使用时呈指数衰减。我们解决了最佳选择使用物品的问题，以便最大限度地提高整体满意度，即，在一段时间内的综合效用。首先，我们证明了总是选择具有最大当前综合效用的项目的简单贪婪启发式可以任意地比最优的更差。其次，我们证明了，即使只有一个人，确定最佳的选择项目的策略是NP-难的。第三，我们证明了一个简单的修改贪心算法，简单地加倍无聊的每个项目是一个可证明的接近最优策略。最后，我们提出了一个实验研究，从查询日志收集的真实世界的数据来比较我们的算法。
英文摘要：
We present a formal model for studying fashion trends, in terms of three parameters of fashionable items: (1) their innate utility; (2) individual boredom associated with repeated usage of an item; and (3) social influences associated with the preferences from other people. While there are several works that emphasize the effect of social influence in understanding fashion trends, in this paper we show how boredom plays a strong role in both individual and social choices. We show how boredom can be used to explain the cyclic choices in several scenarios such as an individual who has to pick a restaurant to visit every day, or a society that has to repeatedly `vote' on a single fashion style from a collection. We formally show that a society that votes for a single fashion style can be viewed as a single individual cycling through different choices. In our model, the utility of an item gets discounted by the amount of boredom that has accumulated over the past; this boredom increases with every use of the item and decays exponentially when not used. We address the problem of optimally choosing items for usage, so as to maximize over-all satisfaction, i.e., composite utility, over a period of time. First we show that the simple greedy heuristic of always choosing the item with the maximum current composite utility can be arbitrarily worse than the optimal. Second, we prove that even with just a single individual, determining the optimal strategy for choosing items is NP-hard. Third, we show that a simple modification to the greedy algorithm that simply doubles the boredom of each item is a provably close approximation to the optimal strategy. Finally, we present an experimental study over real-world data collected from query logs to compare our algorithms.
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