Please use this identifier to cite or link to this item: https://doi.org/10.1145/1807167.1807216
Title: Multiple feature fusion for social media applications
Authors: Cui, B.
Tung, A.K.H. 
Zhang, C.
Zhao, Z.
Keywords: feature fusion
recommendation
search
social media
Issue Date: 2010
Source: Cui, B.,Tung, A.K.H.,Zhang, C.,Zhao, Z. (2010). Multiple feature fusion for social media applications. Proceedings of the ACM SIGMOD International Conference on Management of Data : 435-446. ScholarBank@NUS Repository. https://doi.org/10.1145/1807167.1807216
Abstract: The emergence of social media as a crucial paradigm has posed new challenges to the research and industry communities, where media are designed to be disseminated through social interaction. Recent literature has noted the generality of multiple features in the social media environment, such as textual, visual and user information. However, most of the studies employ only a relatively simple mechanism to merge the features rather than fully exploit feature correlation for social media applications. In this paper, we propose a novel approach to fusing multiple features and their correlations for similarity evaluation. Specifically, we first build a Feature Interaction Graph (FIG) by taking features as nodes and the correlations between them as edges. Then, we employ a probabilistic model based on Markov Random Field to describe the graph for similarity measure between multimedia objects. Using that, we design an efficient retrieval algorithm for large social media data. Further, we integrate temporal information into the probabilistic model for social media recommendation. We evaluate our approach using a large real-life corpus collected from Flickr, and the experimental results indicate the superiority of our proposed method over state-of-the-art techniques. © 2010 ACM.
Source Title: Proceedings of the ACM SIGMOD International Conference on Management of Data
URI: http://scholarbank.nus.edu.sg/handle/10635/40266
ISBN: 9781450300322
ISSN: 07308078
DOI: 10.1145/1807167.1807216
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