A Collaborative Filtering Recommenda-tion Algorithm Based on Category and Influence of Current Items

Abstract

The traditional user-based collaborative filtering (CF) algorithms often suffer from two important problems: Scalability and sparsity because of its memory-based k nearest neighbor query algorithm. ItemBased CF algorithms have been designed to deal with the scalability problems associated with user-based CF approaches without sacrificing recommendation or prediction accuracy. However, itembased CF algorithms still suffer from the data sparsity problems. This paper presents a CF recommendation algorithm, using category information and influence of current items, which is based on the concept of influence set and is a hot topic in information retrieval system.