@inproceedings{e3378337367a4061bf4dfe833c6e659c,
title = "Real-time collaborative filtering recommender systems",
abstract = "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.",
keywords = "Collaborative filtering, Locality sensitive hashing, Real-time, Recommender system",
author = "Huizhi Liang and Haoran Du and Qing Wang",
note = "Publisher Copyright: {\textcopyright} 2014, Australian Computer Society, Inc.",
year = "2014",
language = "English",
series = "Conferences in Research and Practice in Information Technology Series",
publisher = "Australian Computer Society",
pages = "227--232",
editor = "Yanchang Zhao and Yanchang Zhao and Lin Liu and Kok-Leong Ong and Xue Li",
booktitle = "Data Mining and Analytics 2014 - Proceedings of the 12th Australasian Data Mining Conference, AusDM 2014",
address = "Australia",
}