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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 |
(from crpit.com)
(local if available)
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