To address the long-standing data sparsity problem in recommender systems (RSs), cross-
domain recommendation (CDR) has been proposed to leverage the relatively richer …

Traditional recommendation systems are faced with two long-standing obstacles, namely
data sparsity and cold-start problems, which promote the emergence and development of …

Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

Data sparsity is one of the most challenging problems for recommender systems. One
promising solution to this problem is cross-domain recommendation, ie, leveraging …

Predicting anchor links across social networks has important implications to an array of
applications, including cross-network information diffusion and cross-domain …

The increasing popularity and diversity of social media sites has encouraged more and
more people to participate on multiple online social networks to enjoy their services. Each …

The aim of recommender systems is to automatically identify user preferences within
collected data, then use those preferences to make recommendations that help with …

Many recommenders aim to provide relevant recommendations to users by building
personal topic interest profiles and then using these profiles to find interesting contents for …

Graph alignment aims to identify node correspondence across multiple graphs, with
significant implications in various domains. As supervision information is often not available …

Recommender system is an emerging field of research with the advent of World Wide Web
and E-commerce. Recently, an increasing usage of social networking websites plausibly …