Crowd Avoidance and Diversity in Socio-Economic Systems and Recommendation

Crowd Avoidance and Diversity in Socio-Economic Systems and Recommendation
S. Gualdi1, M. Medo1 and Y.-C. Zhang1,2
1 Physics Department, University of Fribourg, CH-1700 Fribourg, Switzerland 2 Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
PACS 07.05.Kf – Data analysis: algorithms and implementation; data management PACS 89.65.-s – Social and economic systems PACS 89.20.-a – Interdisciplinary applications of physics
Abstract –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.

The resulting precision dependencies are shown in Fig. 1. When m ≈ U or b ≈ 0, the allowed occupancy is enough to accommodate all users or repulsion is weak and results are thus identical with the assignment of the object with the highest uiα to each user. When m = 1 or b → ∞ (the fermionic limit), one is forced to assign much inferior objects to some users and the recommendation precision suffers. However, the course of precision is not monotonous: when some intermediate occupancy constraint is applied, precision can be improved and this improvement is further magnified if a sophisticated optimization scheme (Nash/global) is used.