Abstract
Access to sexual information has to be given some restricts on commercial search engine. Compared with filtering porn contents directly, we prefer to recognize porn queries and recommend appropriate ones considering several potential advantages. However, how to recognize them in an automatic way is not a trivial job due that its short length, in most scenarios, doesn’t allow enough information for machine to make correct decision. In this paper, a simple but effective solution is proposed to recognize porn queries as exist in very large query log. Instead of checking purely if there are sensitive words contained in the queries, which may work for some cases but has obvious limitations, we go a little further by collecting and studying the semantic content of queries. Our experiments with real data demonstrate that small cost in training a k-Nearest Neighbor classifier (k-NN) will bring us quite impressive classification performance, especially the recall.
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References
Spink, A., Ozmutlu, H.C., Lorence, D.P.: Web searching for sexual information: an exploratory study. Journal of Information Processing and Management 40, 113–123 (2004)
Cooper, A.: Sexuality and Internet: surfing into the new millennium. Journal of CyberPsychology and Behavior 1(2), 181–187 (1998)
Cooper, A. (ed.): Sex and the Internet: A Guidebook for Clinicians. Brunner-Routledge, New York (2002)
Yang, Y., Zhang, J., Kisiel, B.: A scalability analysis of classifiers in text categorization. In: Proceedings of 26th ACM SIGIR, pp. 96–103 (2003)
Lew, M., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: state of the art and challenges. ACM Transactions on Multimedia Computing, Communications and Applications, 1–19 (2006)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval, ideas, influences and trends of the new age. ACM Computing Surveys 40(2) (2008)
Cramer, J.K., Hersh, W.: Medical image retrieval and automatic annotation: OHSU at ImageCLEF 2007. In: Working Notes for the Cross Language Evaluation Forum (CLEF) Workshop (2007)
Kamvar, M., Baluja, S.: Query suggestions for mobile search: understanding usage patterns. In: Proceedings of the SIGCHI conference on Human Factors in computing systems (CHI) (2008)
Sahami, M.: Mining the Web to determine similarity between words, objects, and communities. In: Proceedings of AAAI FLAIRS (2006)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill Book Company, New York (1983)
Shakhnarovich, G., Darrell, T., Indyk, P. (eds.): Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. MIT Press, Cambridge (2006)
Fu, S., Pi, B., Han, S., Zou, G., Guo, J., Wang, W.: User-centered solution to detect near-duplicate pages on mobile search engine. In: Proceedings of 31th ACM SIGIR Workshop on Mobile IR, Singapore (2008)
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Fu, S. et al. (2009). Simple but Effective Porn Query Recognition by k-NN with Semantic Similarity Measure. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, QM. (eds) Advances in Data and Web Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00672-2_3
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DOI: https://doi.org/10.1007/978-3-642-00672-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00671-5
Online ISBN: 978-3-642-00672-2
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