Conference proceeding
Incremental collaborative filtering via evolutionary co-clustering
Proceedings of the fourth ACM conference on recommender systems, pp.325-328
RecSys '10
09/26/2010
DOI: 10.1145/1864708.1864778
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.
Details
- Title: Subtitle
- Incremental collaborative filtering via evolutionary co-clustering
- Creators
- Mohammad Khoshneshin - University of IowaW Street
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the fourth ACM conference on recommender systems, pp.325-328
- Publisher
- ACM
- Series
- RecSys '10
- DOI
- 10.1145/1864708.1864778
- Language
- English
- Date published
- 09/26/2010
- Academic Unit
- Business Analytics; Computer Science; Bus Admin College; Nursing
- Record Identifier
- 9984380448702771
Metrics
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