The availability of user check-in data in large volume from the rapid growing location-based
social networks (LBSNs) enables a number of important location-aware services. Point-of …

One-class collaborative filtering or collaborative ranking with implicit feedback has been
steadily receiving more attention, mostly due to the “oneclass” characteristics of data in …

Classic Collaborative Filtering (CF) algorithms rely on the assumption that data are static
and we usually disregard the temporal effects in natural user‐generated data. These …

Contexts and social network information have been proven to be valuable information for
building accurate recommender system. However, to the best of our knowledge, no existing …

Implicit feedbacks have recently received much attention in recommendation communities
due to their close relationship with real industry problem settings. However, most works only …

With the rapid development of Internet movie industry social-aware movie recommendation
systems (SMRs) have become a popular online web service that provide relevant movie …

The increasing popularity of real-world recommender systems produces data continuously
and rapidly, and it becomes more realistic to study recommender systems under streaming …

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 …

Many businesses benefit from the business-to-business (B2B) campaign recommendation
strategy. Complex commodities and user profiles restrict the performance of automated B2B …

With the evolution of social media, an enormous amount of information is shared every day.
Recommender systems contribute significantly in handling big data and presenting relevant …