To address the issue of data sparsity and cold-start in recommender system, social
information (eg, user-user trust links) has been introduced to complement rating data for …

Recommender systems elicit the interests and preferences of individuals and make
recommendations accordingly, a main challenge for expert and intelligent systems. An …

In recent years, there are numerous works been proposed to leverage the techniques of
deep learning to improve social-aware recommendation performance. In most cases, it …

The popularity of location-based social networks (LBSNs) has led to a tremendous amount
of user check-in data. Recommending points of interest (POIs) plays a key role in satisfying …

The sparsity of users' preferences can significantly degrade the quality of recommendations
in the collaborative filtering strategy. To account for the fact that the selections of social …

Collaboration usually has a positive effect on researchers' productivity: researchers have
become increasingly collaborative, according to recent studies. Numerous studies have …

There are two key characteristics of users in trust relationships that have been well
studied:(1) users trust their friends with different trust strengths and (2) users play multiple …

Virtual assistants, also known as intelligent conversational systems such as Google's Virtual
Assistant and Apple's Siri, interact with human-like responses to users' queries and finish …

Recent studies have shown that incorporating users' reviews into the collaborative filtering
strategy can significantly boost the recommendation accuracy. A pressing challenge resides …

While users trust the selections of their social friends in recommendation systems, the
preferences of friends do not necessarily match. In this study, we introduce a deep learning …