Traditional data mining usually deals with data from a single domain. In the big data era, we
face a diversity of datasets from different sources in different domains. These datasets …

Among collaborative recommendation approaches, methods based on nearest-neighbors
still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability …

Urbanization's rapid progress has modernized many people's lives but also engendered big
issues, such as traffic congestion, energy consumption, and pollution. Urban computing …

As one of the most successful approaches to building recommender systems, collaborative
filtering (CF) uses the known preferences of a group of users to make recommendations or …

This paper presents an overview of the field of recommender systems and describes the
current generation of recommendation methods that are usually classified into the following …

We have developed a method for recommending items that combines content and
collaborative data under a single probabilistic framework. We benchmark our algorithm …

Recommender systems are becoming increasingly important to individual users and
businesses for providing personalized recommendations. However, while the majority of …

Among collaborative recommendation approaches, methods based on nearest-neighbors
still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability …

The article presents a multidimensional (MD) approach to recommender systems that can
provide recommendations based on additional contextual information besides the typical …

The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.)
enables people to log the location histories with spatio-temporal data. Such real-world …