In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the dev...

Advances in location acquisition and mobile technologies led to the addition of the location dimension to Social Networks (SNs) and to the emergence of a newer class called Location-Based Social Networks (LBSNs). While LBSNs are richer in their model and functions than SNs, they fail so far to attract as many users as SNs. On the other hand, SNs have large amounts of geo-tagged media that are u...

Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the domain, these especially useful, since users often like to listen new songs and discover bands. At same time, group consumption has proliferated this not just physically, past, but virtually rooms or messaging groups created for specific purpose...

Semantic and context knowledge have been envisioned as an appropriate solution for addressing the content heterogeneity and information overload in mobile Web information access, but few have explored their full potential in mobile scenarios, where information objects refer to their physical counterparts, and retrieval is context-aware and personalized for users. We present SMARTMUSEUM, a mobil...

Location and its corollary, distance, are critical concepts in social computing. Recommender systems that incorporate location have generally assumed that the utility of locationawareness monotonically decreases as entities get farther apart. However, it is well known in geography that places that are distant “as the crow flies” can be more similar and connected than nearby places (e.g., by dem...

Recommender systems have always faced the problem of sparse data. In the current era, however, with its demand for highly personalized, real-time, context-aware recommendation, the sparse data problem only threatens to grow worse. Crowdsourcing, specifically, outsourcing micro-requests for information to the crowd, opens new possibilities to fight the sparse data challenge. In this paper, we la...

Recent technological trends such as cloud computing, wireless communication, and wireless sensor networks provide a strong infrastructure and offer a true enabler for health information technology services over the Internet. This system is based on the cloud computing environment, integrating mobile communication technology, context-aware technology, and wireless sensor networks to build a mobi...

Food based dietary guidelines are insufficiently followed by consumers. One of the principal explanations of this failure is that they are too general and do not take into account eating habits. Providing personalized dietary recommendations via nutrition recommender system can hence help people improve their eating habits. Understanding eating habits is a keystone in order to build a context a...