Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Matrix Fac...
We present the design, implementation and evaluation of a new geotagging service, Gloe, that makes it easy to find, rate and recommend arbitrary on-line content in a mobile settin...
Thomas Sandholm, Hang Ung, Christina Aperjis, Bern...
Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the exemplars of a new generation of social, real-time web services and w...
An increasing number of social networking platforms are giving users the option to endorse entities that they find appealing, such as videos, photos, or even other users. We defin...
Recommender systems have emerged as an effective decision tool to help users more easily and quickly find products that they prefer, especially in e-commerce environments. However...
We present and evaluate various content-based recommendation models that make use of user and item profiles defined in terms of weighted lists of social tags. The studied approach...
Collaborative filtering is a popular approach for building recommender systems. Current collaborative filtering algorithms are accurate but also computationally expensive, and so ...
A recommender system has to collect users' preference data. To collect such data, rating or scoring methods that use rating scales, such as good-fair-poor or a five-point-sca...
The majority of recommender systems are designed to make recommendations for individual users. However, in some circumstances the items to be selected are not intended for persona...
Linas Baltrunas, Tadas Makcinskas, Francesco Ricci