Towards Dynamic User Intention in Sequential Recommendation

User intention is an important factor to be considered for recommender systems. Different from inherent user preference addressed in traditional recommendation algorithms, which is generally static and consistent, user intention always changes dynamically in different contexts. Recent studies (represented by sequential recommendation) begin to focus on predicting what users want beyond what users like, which can better capture dynamic user intention and have attracted a surge of interest. However, user intention modeling is non-trivial because it is generally influenced by various factors, such as repeat consumption behavior, item relation, temporal dynamics, etc. To better capture dynamic user intention in sequential recommendation, we plan to investigate the influential factors and construct corresponding models to improve the performance. We also want to develop an adaptive way to model temporal evolutions of the effects caused by different factors. Based on the above investigations, we further plan to integrate these factors to deal with extremely long history sequences, where long-term user preference and short-term user demand should be carefully balanced.