Matrix factorization is a successful technique for building collaborative filtering systems. While it works well on a large range of problems, it is also known for requiring signi...
Alexandros Karatzoglou, Alexander J. Smola, Markus...
Server-based collaborative filtering systems have been very successful in e-commerce and in direct recommendation applications. In future, they have many potential applications in...
Collaborative filtering systems predict a user's interest in new items based on the recommendations of other people with similar interests. Instead of performing content index...
Jonathan L. Herlocker, Joseph A. Konstan, John Rie...
Collaborative Filtering systems suggest items to a user because it is highly rated by some other user with similar tastes. Although these systems are achieving great success on we...
Collaborative filtering is based on the premise that people looking for information should be able to make use of what others have already found and evaluated. Current collaborati...
Collaborative filtering systems help address information overload by using the opinions of users in a community to make personal recommendations for documents to each user. Many c...
Badrul M. Sarwar, Joseph A. Konstan, Al Borchers, ...