Collaborative filtering on streaming data with interest-drifting

Abstract

Collaborate filtering is one of the most popular recommendation algorithms. Most collaborative filtering algorithms work with static data. This paper introduces a novel approach to providing recommendations using collaborative filtering when user rating is arrived over an incoming data stream. In this case a large number of data records can arrive rapidly making it impossible to save all of them for later analysis. Moreover, user interests may change over time. By dynamically building a decision tree for every item as data arrive, the incoming data stream is used effectively with a trade off between catching up the changes of users interests and accuracy. By adding a simple step using a hierarchy of items taxonomy, it is also possible to further improve the predicted ratings made by each decision tree and generate recommendations in realtime. Empirical studies with the dynamically built decision trees show that our algorithm works effectively and improves the overall prediction accuracy.