Application of Kullback-Leibler divergence for short-term user interest detection
This addresses the need for reactive algorithms in e-commerce to improve user experience, but it appears incremental as it applies an existing information theory concept to a specific domain.
The paper tackles the problem of detecting short-term user interests in e-commerce recommender systems, where classical static methods are insufficient, by introducing a mathematical framework based on Kullback-Leibler divergence to enhance recommendations.
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.