Incremental collaborative filtering via evolutionary co-clustering

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. Categories and Subject Descriptors H.2.8 [Database Applications]: Data Mining General Terms algorithms, theory. Keywords Incremental collaborative filtering, co-clustering, evolutionary algorithm, ensembles.

• Type: Conference
• Year: 2010
• Where: RECSYS
• Authors: Mohammad Khoshneshin, W. Nick Street
• Keywords: Collaborative Filtering, Collaborative Filtering Algorithms, Collaborative Filtering Model, Control Systems, RECSYS 2010