Cluster searching strategies for collaborative recommendation systems

Date

2013-05-01

Author

Altıngövde, İsmail Sengör
Subakan, Oezlem Nurcan
Ulusoy, Ozgur

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individualistic strategy which initially clusters the users and then exploits the members within clusters, but not just the cluster representatives, during the recommendation generation stage. We provide an efficient implementation of this strategy by adapting a specifically tailored cluster-skipping inverted index structure. Experimental results reveal that the individualistic strategy with the cluster-skipping index is a good compromise that yields high accuracy and reasonable scalability figures.

Subject Keywords

Collaborative filtering, Clustering, Inverted index

URI

https://hdl.handle.net/11511/43997

Collections

Department of Computer Engineering, Article