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.
These guys begin by considering items that are adversely affected when too many people use them. For example, recommending a beach or a picnic spot because it is quiet can end up destroying the peace that gives it value. Similarly, restaurant recommendations can lead to overcrowding or difficulty getting a table which again makes the dining experience unpleasant.
So Gualdi and co ask how to deal with recommendations for objects whose value diminishes with the number of people who use it. And their conclusions are surprising and counterintuitive. They say that this approach not only works well in these unusual circumstances but also improves conventional recommendations for objects or resources that are not in limited supply.
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.
Crowd Avoidance and Diversity in Socio-Economic Systems and Recommendation