Real-time Collaborative Filtering Recommender Systems

Liang, H., Du, H. and Wang, Q.

    Recommender systems can help users deal with the information overload issue. Many real-world communities such as social media websites require realtime recommendation making to capture the recent updates of the communities. This brings challenges to existing approaches which mainly build recommendation models at offline. In this paper, we discuss real-time collaborative filtering recommendation approaches. The proposed approaches use locality sensitive hashing (LSH) to construct user or item blocks, which facilitate real-time neighborhood formation and recommendation making. The experiments conducted on a Twitter dataset demonstrate the effectiveness of the proposed approaches.
Cite as: Liang, H., Du, H. and Wang, Q. (2014). Real-time Collaborative Filtering Recommender Systems. In Proc. Twelfth Australasian Data Mining Conference (AusDM14) Brisbane, Australia. CRPIT, 158. Li, X., Liu, L., Ong, K.L. and Zhao, Y. Eds., ACS. 227-231
pdf (from crpit.com) pdf (local if available) BibTeX EndNote GS