Better online shopping recommendations thanks to particle physics

MIT Technology Review : Online shopping sites make use of various algorithms to suggest items for you to purchase based on what you and other users have purchased in the past. One effect of such recommendations is that some locales or products suffer from the sudden influx of people directed to them by the recommendation. To try to avoid this problem, a team of researchers led by Stanislao Gualdi of the University of Fribourg in Switzerland has applied a feature of particle physics. At the atomic level, particles tend to occupy the most energetically favorable states; but the number of particles that can occupy any given state depends on the type of particle. Gualdi drew a parallel between this concept and that of commercial products, which can be shared by either many or just a few people. The team developed a model that can limit the number of users allowed for a given product. When testing their model against empirical data of DVD rentals, they found that limiting rentals ensures that a wider range of DVDs get rented. And the more DVDs rented, the broader the range and accuracy of the ensuing recommendations. The overall effect was a healthier rental system. However, whether the model would work for actual retailers, who focus on maximizing their profits, is uncertain.