A Scalable Collaborative Filtering Framework Based on Co-Clustering

Collaborative filtering-based recommender systems, which automatically predict preferred products of a user using known preferences of other users, have become extremely popular in recent years due to the increase in web-based activities such as e-commerce and online content distribution. Current collaborative filtering techniques such as correlation and SVD based methods provide good accuracy, but are computationally very expensive and can only be deployed in static off-line settings where the known preference information does not change with time. However, a number of practical scenarios require dynamic real-time collaborative filtering that can allow new users, items and ratings to enter the system at a rapid rate. In this paper, we consider a novel collaborative filtering approach based on a recently proposed weighted co-clustering algorithm [3] that involves simultaneous clustering of users and items. We design incremental and parallel versions of the co-clustering algorithm ...

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Thomas George, Srujana Merugu
• Keywords: Collaborative, Collaborative filtering-based Recommender, Data Mining, ICDM 2005, Real-time Collaborative