Real-time Context-aware Social Media Recommendation

Zhou, X and Qin, D 2019, 'Real-time Context-aware Social Media Recommendation', VLDB Journal, vol. 28, no. 2, pp. 197-219.

Document type: Journal Article
Collection: Journal Articles

Title Real-time Context-aware Social Media Recommendation
Author(s) Zhou, X
Qin, D
Year 2019
Journal name VLDB Journal
Volume number 28
Issue number 2
Start page 197
End page 219
Total pages 23
Publisher Association for Computing Machinery
Abstract Social media recommendation has attracted great attention due to its wide applications in online advertisement and entertainment etc. Since contexts highly affect social user preferences, great effort has been put into context-aware recommendation in recent years. However, existing techniques cannot capture the optimal context information that is most discriminative and compact from a large number of available features flexibly for effective and efficient context-aware social recommendation. To address this issue, we propose a generic framework for context-aware recommendation in shared communities, which exploits the characteristics of media content and contexts. Specifically, we first propose a novel approach based on the correlation between a feature and a group of other ones for selecting the optimal features used in recommendation, which fully removes the redundancy. Then, we propose a graph-based model called \emph{content-context interaction graph} (CCIG), by analysing the metadata content and social contexts, and the interaction between attributes. Finally, we design hash-based index over Apache Storm for organizing and searching the media database in real time. Extensive experiments have been conducted over large real media collections to prove the high effectiveness and efficiency of our proposed framework.
Subject Database Management
Keyword(s) Social Media Recommendation
Feature Selection
Content-context Interaction
DOI - identifier 10.1007/s00778-018-0524-7
Copyright notice © Springer-Verlag GmbH Germany, part of Springer Nature 2018
ISSN 1066-8888
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
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