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...
Abstract. In this paper we propose an incremental item-based collaborative filtering algorithm. It works with binary ratings (sometimes also called implicit ratings), as it is typi...
We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determin...
Abstract. As the amount of information available to users continues to grow, filtering wanted items from unwanted ones becomes a dominant task. To this end, various collaborative-f...
Information flows in a network where individuals influence each other. The diffusion rate captures how efficiently the information can diffuse among the users in the network. We p...