The VLDB Journal

, Volume 28, Issue 2, pp 197–219 | Cite as

Real-time context-aware social media recommendation

  • Xiangmin ZhouEmail author
  • Dong Qin
  • Lei Chen
  • Yanchun Zhang
Regular Paper


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 content–context interaction graph, 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.


Social media recommendation Feature selection Content–context interaction Real-time 



The work is partially supported by ARC project DP140100841, the Hong Kong RGC GRF Project 16207617, the National Science Foundation of China (NSFC) under Grants (No. 61729201, No.61332013, No.61572139), Science and Technology Planning Project of Guangdong Province, China, No. 2015B010110006, Huawei Co.Ltd Collaboration Project, YBCB2009041-45, Hong Kong ITC ITF grants ITS/391/15FX and ITS/212/16FP, and Microsoft Research Asia Collaborative Research Grant. The authors would like to thank Wenqiang Shao, Yang Li, Sheng Wang, and Liangjun Song for useful discussions.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ScienceRMIT UniversityMelbourneAustralia
  2. 2.Department of Computer Science and EngineeringHong Kong University of Science and TechnologyHongkongChina
  3. 3.Centre for Applied InformaticsVictoria UniversityFootscrayAustralia
  4. 4.Cyberspace Institute of Advanced Technology (CIAT)Guangzhou UniversityGuangzhouChina

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