Sex and the Internet: A Guide Book for Clinicians
Sex and the Internet: A Guide Book for Clinicians
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A scalability analysis of classifiers in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Web searching for sexual information: an exploratory study
Information Processing and Management: an International Journal
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Query suggestions for mobile search: understanding usage patterns
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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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.