In today's mobile applications, it becomes more and more important to have a broader view on knowledge about a certain domain when generating contextual and semantic recommendations. Data that provides additional and useful information to the ...

Multi-lingual text processing is important for content-based and hybrid recommender systems. It helps recommender systems extract content information from broader sources. It also enables systems to recommend items in a user's native language. We ...

Explicit and implicit feedback exhibits different characteristics of users' preferences with both pros and cons. However, a combination of these two types of feedback provides another paradigm for recommender systems (RS). Their combination in a user ...

Collaborative recommendation is effective at representing a user's overall interests and tastes, and finding peer users that can provide good recommendations. However, it remains a challenge to make collaborative recommendation sensitive to a user's ...

The study reported in this paper is an attempt to improve content-based recommendation in CoMeT, a social system for sharing information about research colloquia in Carnegie Mellon and University of Pittsburgh campuses. To improve the quality of ...

The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find ...

Providing relevant recommendations requires access to user profile data. Current social networking ecosystems allow third party services to request user authorisation for accessing profile data, thus enabling cross-domain recommendation. However these ...

We present a preliminarily study on the influence of different sources of information in Web 2.0 systems on recommendation. Aiming to identify which are the sources of information (ratings, tags, social contacts, etc.) most valuable for recommendation, ...