A scalable collaborative filtering framework based on co-clustering

Collaborative filtering-based recommender systems 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 (CF) techniques such as correlation and SVD based methods provide good accuracy, but are computationally expensive and can be deployed only in static off-line settings. 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 CF approach based on a recently proposed weighted co-clustering algorithm [1] that involves simultaneous clustering of users and items. We design incremental and parallel versions of the co-clustering algorithm and use it to build an efficient real-time CF framework. Empirical evaluation demonstrates that our approach provides an accuracy comparable to that of the correlation and matrix factorization based approaches at a much lower computational cost.