Cross domain recommender systems (CDRS) can assist recommendations in a target
domain based on knowledge learned from a source domain. CDRS consists of three …

Online platforms can be divided into information-oriented and social-oriented domains. The
former refers to forums or E-commerce sites that emphasize user-item interactions, like Trip …

The proliferation of e-commerce sites and online social media has allowed users to provide
preference feedback and maintain profiles in multiple systems, reflecting a variety of their …

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 …

The increasing popularity of social media encourages more and more users to participate in
various online activities and produces data in an unprecedented rate. Social media data is …

A social recommendation system has attracted a lot of attention recently in the research
communities of information retrieval, machine learning, and data mining. Traditional social …

Social e-commerce, as a new concept of e-commerce, uses social media as a new prevalent
platform for online shop**. Users are now able to view, add to cart, and buy products …

With the explosion of knowledge and information in the big data era, learning new things
efficiently is of crucial significance. Despite recent development of e-learning techniques …

Abstract Point-of-Interest (POI) recommendation is one of the most essential tasks in LBSNs
to help users discover new interesting locations, especially when users travel out of town or …

As location-based social networks (LBSNs) rapidly grow, it is a timely topic to study how to
recommend users with interesting locations, known as points-of-interest (POIs). Most …