Most recommendation systems employ variations of Collaborative Filtering (CF) for formulating suggestions of items relevant to users’ interests. However, CF requires expensive co...
Manos Papagelis, Ioannis Rousidis, Dimitris Plexou...
Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large ...
Jun Wang, Arjen P. de Vries, Marcel J. T. Reinders
Collaborative filtering requires a centralized rating database. However, within a peer-to-peer network such a centralized database is not readily available. In this paper, we pro...
Jun Wang, Johan A. Pouwelse, Reginald L. Lagendijk...
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...
Item-based Collaborative Filtering (CF) algorithms have been designed to deal with the scalability problems associated with traditional user-based CF approaches without sacrificin...