社会经济系统中的人群回避和多样性及建议
Crowd Avoidance and Diversity in Socio-Economic Systems and Recommendation
摘要：
推荐系统推荐对象，而不考虑其过度拥挤的潜在不利影响。我们通过引入避免人群的推荐来解决这个缺点，其中每个对象只能由有限数量的用户共享，或者对象效用随着共享用户的数量而减少。我们使用真实数据表明，与预期相反，这些约束的引入即使在过度拥挤并不有害的系统中也能提高推荐的准确性和多样性。所观察到的准确性的提高解释在消除潜在的偏见的推荐方法。最后，我们提出了一种方法来模拟人工社会经济系统与人群回避，并获得第一个分析结果。
英文摘要：
Recommender systems recommend objects regardless of potential adverse effects of their overcrowding. We address this shortcoming by introducing crowd-avoiding recommendation where each object can be shared by only a limited number of users or where object utility diminishes with the number of users sharing it. We use real data to show that contrary to expectations, the introduction of these constraints enhances recommendation accuracy and diversity even in systems where overcrowding is not detrimental. The observed accuracy improvements are explained in terms of removing potential bias of the recommendation method. We finally propose a way to model artificial socio-economic systems with crowd avoidance and obtain first analytical results.
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