Most modern recommender systems predict users' preferences with two components: user
and item embedding learning, followed by the user-item interaction modeling. By utilizing …

Learning user representation is a critical task for recommendation systems as it can encode
user preference for personalized services. User representation is generally learned from …

Recent research explores incorporating knowledge graphs (KG) into e-commerce
recommender systems, not only to achieve better recommendation performance, but more …

Review-Based Recommender Systems (RBRS) have attracted increasing research interest
due to their ability to alleviate well-known cold-start problems. RBRS utilizes reviews to …

Given the growing ubiquity of emojis in language, there is a need for methods and resources
that shed light on their meaning and communicative role. One conspicuous aspect of emojis …

Numerous reviews are posted every day on travel information sharing platforms and sites.
Hotels want to develop a customer recommender system to quickly and effectively identify …

Review-based recommender systems explore semantic aspects of users' preferences by
incorporating user-generated reviews into rating-based models. Recent works have …

Textual reviews contain rich semantic information that is useful for making better
recommendation, as such semantic information may indicate more fine-grained preferences …

Real recommendation systems contain various features, which are often high-dimensional,
sparse, and difficult to learn effectively. In addition to numerical features, user reviews …

Sequential recommendation is dedicated to offering items of interest for users based on their
history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items …