[Objective] his study explores the structure of user interest hierarchy, as well as its evolution laws, aiming to improve the quality of personalized information services. [Methods] First, we used the LDA topic model to retrieve the topics of users’ tags. Then, we calculated the tag’s degree of interests, which were combined with their topics to identify user’s interests. Finally, we created the “core-edge” structure for user’s interests based on the interest network to analyze the evolution laws of their hierarchy. [Results] The “core-edge” structure of user’s interests gradually converged and became stable with the determination of interest domain. The evolution of user interest hierarchy in time series mainly included three types: always in the core layer, the core layer faded to the edge layer, and the edge layer promoted to the core layer. [Limitations] More research is needed to predict user’s interests in future time nodes. [Conclusions] This proposed method could accurately evaluate the existing users’ dynamic interests, and the evolution laws of their hierarchy, which optimizes personalized information services.
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