With the rapid development of online social recommendation system, substantial methods
have been proposed. Unlike traditional recommendation system, social recommendation …

Recommender systems have become an important instrument to connect people to
information. Sparse, complex, and rapidly growing data presents new challenges to …

This article presents a novel model named Adversarial Auto-encoder Domain Adaptation to
handle the recommendation problem under cold-start settings. Specifically, we divide the …

In recommender systems, click behaviors play a fundamental role in mining users' interests
and training models (clicked items as positive samples). Such signals are implicit feedback …

Learning representations of both user interests and item characteristics is essentially
important for recommendation tasks. Although graph neural network-based methods have …

Many state-of-the-art recommendation systems leverage explicit item reviews posted by
users by considering their usefulness in representing the users' preferences and describing …

Filtering information on the Internet and recommending the right choices is more than
important for Internet users and various businesses that offer products and services …

Context-aware recommender systems (CARS), which capture user preferences by
incorporating user interactions and side information, have attracted widespread attention in …

Recommendation systems provide personalized service to users and aim at suggesting to
them items that they may prefer. There is an increasing requirement of next-item …

In this article, we study the task of user profiling in question answering communities (QACs).
Previous user profiling algorithms suffer from a number of defects: they regard users and …