Sequential recommendation aims at predicting users' preferences based on their historical
behaviors. However, this recommendation strategy may not perform well in practice due to …

Transformer-based sequential recommenders are very powerful for capturing both short-
term and long-term sequential item dependencies. This is mainly attributed to their unique …

Sequential recommendation holds the promise of understanding user preference by
capturing successive behavior correlations. Existing research focus on designing different …

Recent advances in path-based explainable recommendation systems have attracted
increasing attention thanks to the rich information provided by knowledge graphs. Most …

Sequential recommendation as an emerging topic has attracted increasing attention due to
its important practical significance. Models based on deep learning and attention …

Sequential Cross-Domain Recommendation (CDR) has been popularly studied to utilize
different domain knowledge and users' historical behaviors for the next-item prediction. In …

Cross-domain recommendation (CDR) aims at leveraging the correlation of users' behaviors
in both the source and target domains to improve the user preference modeling in the target …

The widespread use of online recruitment services has led to an information explosion in the
job market. As a result, recruiters have to seek intelligent ways for Person-Job Fit, which is …

The growth of data in recent years has motivated the emergence of deep learning in many
Computer Sciences related fields including Recommender System (RS). Deep learning has …

Sequential recommendation is a stream of studies on recommender systems, which focuses
on predicting the next item a user interacts with by modeling the dynamic sequence of user …