Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Sequential recommendation is the task of predicting the next items for users based on their
interaction history. Modeling the dependence of the next action on the past actions …

Human behaviors in recommendation systems are driven by many high-level, complex, and
evolving intentions behind their decision making processes. In order to achieve better …

Sequential recommendation tries to model the binary correlations among users and items in
a sequence to provide accurate recommendations. However, user behaviors are influenced …

Sequential recommendation systems aim to exploit users' sequential behavior patterns to
capture their interaction intentions and improve recommendation accuracy. Existing …

Sequential recommender systems (SRSs) aim to predict the subsequent items which may
interest users via comprehensively modeling users' complex preference embedded in the …

Sequential recommendation has attracted a lot of attention from both academia and industry.
Since item embeddings directly affect the recommendation results, their learning process is …

A session-based recommender system (SBRS) captures users' evolving behaviors and
recommends the next item by profiling users in terms of items in a session. User intent and …

Current session-based recommender systems (SBRSs) mainly focus on maximizing
recommendation accuracy, while few studies have been devoted to improve diversity …

The studies on users' purchase intentions based on e-commerce data are of great
significance to marketers, buyers, and society. Current studies on users' intentions with …