Abstract
The increasing popularity of location-based social networks encourages more and more users to share their experience. It deeply impact the decision of the other users. In this paper, we study the problem of top-K remarkable comments over textual streaming data. We first study how to efficiently identify the mendacious comments. Through using a novel machine learning technique named ELM, we could filter most of mendacious comments. We then study how to maintain these vital comments. For one thing, we propose a two-level index to maintain their position information. For another, we employ domination transitivity to remove meaningless comments. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the proposed algorithms.
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Acknowledgments
The work is partially supported by the National Natural Science Foundation of China for Outstanding Young Scholars (No. 61322208), the National Basic Research Program of China (973 Program) (No. 2012CB316201), the Joint Research Fund for Overseas Natural Science of China (No. 61129002), the National Natural Science Foundation of China for Key Program (No. 61572122), the National Natural Science Foundation of China (Nos. 61272178, 61572122, 61173029).
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Zhu, R., Wang, B., Wang, G. (2016). Continuous Top-K Remarkable Comments over Textual Streaming Data Using ELM. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_13
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DOI: https://doi.org/10.1007/978-3-319-28373-9_13
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