Abstract
We propose a unified fusion framework for time-related rank, applied to find valuable posts or recommend answers in threaded discussion communities. In our model, we simultaneously consider the special structure and semantics of threaded discussion communities. As for the structure, we construct a time-related rank model with respect to reply posts analysis and attain an initial rank result. Concurrently, we reconstruct semantic trees from raw statistical features (e.g. term frequency and document length) to latent semantics and topics. With a more robust similarity computation, we produce several semantic trees. For each tree, we again compute the time-related rank score and get a series of rank results. Finally, we fuse our results in the unified fusion framework incorporating quality measures to make a final decision. Our model can be easily extended when new features or models are added. Experimental results show that our model contributes satisfactory results.
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Acknowledgments
This work is partly supported by the 973 basic research program of China (Grant No. 2014CB349303), the National 863 High-Tech R&D Program of China (Grant No. 2012AA012504), the National Science Foundation of China (Grant No. 61379098, No. 61103056), the Natural Science Foundation of Beijing (Grant No. 4121003), and The Project Supported by Guangdong Natural Science Foundation (Grant No. S2012020011081).
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You, Q., Hu, W., Wu, O., Zuo, H. (2014). A Unified Fusion Framework for Time-Related Rank in Threaded Discussion Communities. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_46
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DOI: https://doi.org/10.1007/978-3-319-13186-3_46
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