Adaptation to Drifting User's Interests

Abstract

In recent years, many systems have been developed which aim at helping users to find pieces of information or other objects that are in accordance with their personal interests. In these systems, machine learning methods are often used to acquire the user interest profile. Frequently user interests drift with time. The ability to adapt fast to the current user's interests is an important feature for recommender systems. This paper presents a method for dealing with drifting interests by introducing the notion of gradual forgetting. Thus, the last observations should be more "important" for the learning algorithm than the old ones and the importance of an observation should decrease with time. The conducted experiments with a recommender system show that the gradual forgetting improves the ability to adapt to drifting user's interests. Experiments with the STAGGER problem provide additional evidences that gradual forgetting is able to improve the prediction accuracy on drifting concepts (incl. drifting user's interests).