Improving session-based temporal recommendation by using dynamic clustering

Abstract

Modelling users’ dynamic preference for personalized temporal recommendation has been a hot research topic. Traditional dynamic recommendation models divide a user’s interaction history into fixed-sized windows to learn the user’s evolving preference. This strategy however worsens the issue of data sparsity in that some sessions may have very little or even no interaction for preference inference. To alleviate the data sparsity issue and avoid errors due to data imputation that is commonly adopted by existing models, a novel session-based dynamic recommendation model that divides a user’s interaction history with dynamic window size is proposed. An empirical study on the users’ activity life cycles using real-world dataset is conducted to demonstrate the nonuniformness and aggregation of the users’ behavior patterns on the time dimension. Based on the study, a user’s interaction history is divided with dynamic temporal window size by using dynamic clustering. The user’s evolving profile is then constructed by modeling her preference in each session using Latent Dirichlet Allocation (LDA) and a time sensitive weighting scheme. Our dynamic model is designed for two major recommendation tasks: (1) top-N recommendation, which is provided by measuring the relevance of probabilistic topic distribution between the user’s profile in the next temporal domain and each candidate item, (2) rating prediction that is achieved by finding $K$ nearest user/item neighbors based the similarity of probabilistic topic distribution between users/items. Extensive empirical experiments over two real datasets demonstrate the effectiveness and superiority of our method by comparing to representative temporal dynamic methods.