Application of Kullback-Leibler divergence for short-term user interest detection

Classical approaches in recommender systems such as collaborative filtering are concentrated mainly on static user preference extraction. This approach works well as an example for music recommendations when a user behavior tends to be stable over long period of time, however the most common situation in e-commerce is different which requires reactive algorithms based on a short-term user activity analysis. This paper introduces a small mathematical framework for short-term user interest detection formulated in terms of item properties and its application for recommender systems enhancing. The framework is based on the fundamental concept of information theory Kullback-Leibler divergence.