Particle Physics, Crowd Avoidance, Socio-Economic Systems and Recommendation Engines
Some items are adversely affected when too many people use them. Surprisingly, the same physics that govern the behaviour of photons and electrons may also improve online shopping recommendations and help avoid crowds.
Technology Review (The Physics arXiv Blog), January 15, 2013
Most online shoppers will be familiar with phrases of the type "You liked X, so you might like Y" that are generated by the current crop of recommendation engines. These play an increasingly important role for online retailers since they can increase sales by significant amounts.
... Stanislao Gualdi at the University of Fribourg in Switzerland and a couple of pals say they've found a surprising new twist in this black art that increases the accuracy of these systems.
The approach these guys take is based on thinking used in particle physics, where particles tend to occupy the most energetically favourable states. If the particles are bosons, such as photons, there is no limit to the number that can occupy a given state. But if they are fermions, like electrons, their physical properties dictate that no two can occupy the same state. Clearly the resulting distribution of these different types of particles is entirely different.The analogy here is with goods that any number of people can share or that only one person can have.
CORNELL UNIVERSITY LIBRARY - CROWD AVOIDANCE AND DIVERSITY IN SOCIO-ECONOMIC SYSTEMS AND RECOMMENDATION
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.
That's so far outside my knowledge zone that it's just cool. Particle physics and recommendation engines? Awesome....
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