Semantically Enhanced Collaborative Filtering on the Web

Item-based Collaborative Filtering (CF) algorithms have been designed to deal with the scalability problems associated with traditional user-based CF approaches without sacrificing recommendation or prediction accuracy. Item-based algorithms avoid the bottleneck in computing user-user correlations by first considering the relationships among items and performing similarity computations in a reduced space. Because the computation of item similarities is independent of the methods used for generating predictions, multiple knowledge sources, including structured semantic information about items, can be brought to bear in determining similarities among items. The integration of semantic similarities for items with rating- or usage-based similarities allows the system to make inferences based on the underlying reasons for which a user may or may not be interested in a particular item. Furthermore, in cases where little or no rating (or usage) information is available (such as in the case ...

• Type: Conference
• Year: 2003
• Where: EWMF
• Authors: Bamshad Mobasher, Xin Jin, Yanzan Zhou
• Keywords: EWMF 2003, Item Similarities, Semantic Similarities, Sparse Data Sets