To understand users’ acceptance of the emerging trend of personality-based recommenders (PBR), we evaluated an existing PBR using the technology acceptance model (TAM). We also ...
Short search engine queries do not provide contextual information, making it difficult for traditional search engines to understand what users are really requesting. One approach ...
Ajith Kodakateri Pudhiyaveetil, Susan Gauch, Hiep ...
Collaborative Filtering (CF) recommendations are computed by leveraging a historical data set of users’ ratings for items. It assumes that the users’ previously recorded ratin...
We propose FriendSensing, a framework that automatically suggests friends to mobile social-networking users. Using short-range technologies (e.g., Bluetooth) on her mobile phone, ...
Recommender systems based on user feedback rank items by aggregating users’ ratings in order to select those that are ranked highest. Ratings are usually aggregated using a weig...
Florent Garcin, Boi Faltings, Radu Jurca, Nadine J...
Besides the rating information, an increasing number of modern recommender systems also allow the users to add personalized tags to the items. Such tagging information may provide...
We propose a novel collaborative recommendation approach to take advantage of the information available in user-created lists. Our approach assumes associations among any two item...
We propose a recommendation technique that works by collecting text descriptions of items and using this textual aura to compute the similarity between items using techniques draw...
Stephen J. Green, Paul Lamere, Jeffrey Alexander, ...
Conversation double pivots recommend target items related to a source item, based on co-mentions of source and target items in online forums. We deployed several variants on the d...
Different buyers exhibit different purchasing behaviors. Some rush to purchase new products while others tend to be more cautious, waiting for reviews from people they trust. In...