In e-learning recommender systems, interpersonal information between learners is very
scarce, which makes it difficult to apply collaborative filtering (CF) techniques to achieve …

In recommender systems, cold-start issues are situations where no previous events, eg,
ratings, are known for certain users or items. In this paper, we focus on the item cold-start …

Understanding temporal dynamics has proved to be highly valuable for accurate
recommendation. Sequential recommenders have been successful in modeling the …

User-generated product reviews contain a lot of valuable information including users'
opinions on products and product features that is not fully exploited by the current …

This paper targets to a novel but practical recommendation problem named exact-K
recommendation. It is different from traditional top-K recommendation, as it focuses more on …

Sequential recommendation is one of the main tasks in recommender systems, where the
next action (eg, purchase, visit, and click) of the user is predicted based on his/her past …

Most existing recommender systems leverage the primary feedback only, despite the fact
that users also generate a large amount of auxiliary feedback. These feedback usually …

Binary representation of users and items can dramatically improve efficiency of
recommendation and reduce size of recommendation models. However, learning optimal …

Attention is the behavioral and cognitive process of selectively concentrating on small
fraction of information while ignoring other perceivable information. Thus, user's attention …

Over the years, model-based approaches have shown their effectiveness in computing
recommendation lists in different domains and settings. By relying on the computation of …