Incremental collaborative filtering via evolutionary co-clustering

Authors:
Mohammad Khoshneshin;W. Nick Street
Affiliations:
University of Iowa, Iowa City, IA, USA;University of Iowa, Iowa City, IA, USA
Venue:
Proceedings of the fourth ACM conference on Recommender systems
Year:
2010

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Abstract

Collaborative filtering is a popular approach for building recommender systems. Current collaborative filtering algorithms are accurate but also computationally expensive, and so are best in static off-line settings. It is desirable to include the new data in a collaborative filtering model in an online manner, requiring a model that can be incrementally updated efficiently. Incremental collaborative filtering via co-clustering has been shown to be a very scalable approach for this purpose. However, locally optimized co-clustering solutions via current fast iterative algorithms give poor accuracy. We propose an evolutionary co-clustering method that improves predictive performance while maintaining the scalability of co-clustering in the online phase.