Multi-granularity user interest modeling and interest drift detection

Abstract

Since the advent of Web 2.0 culture, there as been an explosion of data on the internet. The traditional service model based on the search engine can no longer meet the increasing demand for personalized service. Taking the Douban film review platform as an example in this paper, we propose a method to model user preferences and detect preference drift. Based on a hierarchical topic tree and tilted time window, we design a hierarchical classification tree, named HAT-tree, to maintain the history of the user’s preferences at multi-topic and multi-time granularity. We identify the user’s primary historical preferences, predict their future primary preferences and also detect user preference drift. The proposed algorithm can find the user’s long-term and short-term preferences, detect the user’s explicit and implicit preference drift, and highlight the importance of the user’s more recent preferences. Many experiments are carried out on multiple data sets, and the experimental results show that the proposed method is more accurate than other similar algorithms of user preference drift detection.