Abstract
With more and more commercial activities moving onto the Internet, people tend to purchase what they need through Internet or conduct some online research before the actual transactions happen. For many Web users, their online commercial activities start from submitting a search query to search engines. Just like the common Web search queries, the queries with commercial intention are usually very short. Recognizing the queries with commercial intention against the common queries will help search engines provide proper search results and advertisements, help Web users obtain the right information they desire and help the advertisers benefit from the potential transactions. However, the intentions behind a query vary a lot for users with different background and interest. The intentions can even be different for the same user, when the query is issued in different contexts. In this paper, we present a new algorithm framework based on skip-chain conditional random field (SCCRF) for automatically classifying Web queries according to context-based online commercial intention. We analyze our algorithm performance both theoretically and empirically. Extensive experiments on several real search engine log datasets show that our algorithm can improve more than 10% on F1 score than previous algorithms on commercial intention detection.
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References
Beitzel, S.M., Jensen, E.C., Frieder, O., Grossman, D.A., Lewis, D.D., Chowdhury, A., Kolcz, A.: Automatic web query classification using labeled and unlabeled training data. In: SIGIR 2005, pp. 581–582 (2005)
Broder, A.Z.: A taxonomy of web search. SIGIR Forum 36(2), 3–10 (2002)
Cao, H., Hu, D.H., Shen, D., Jiang, D., Sun, J.-T., Chen, E., Yang, Q.: Contextaware query classification. In: SIGIR 2009 (2009)
Dai, H.K., Zhao, L., Nie, Z., Wen, J.-R., Wang, L., Li, Y.: Detecting online commercial intention (oci). In: WWW 2006, pp. 829–837 (2006)
Jansen, B.J.: The effect of query complexity on web searching results. Information Research 6(1) (2000)
Jansen, B.J., Booth, D.L., Spink, A.: Determining the user intent of web search engine queries. In: WWW 2007, pp. 1149–1150 (2007)
Kang, I.-H., Kim, G.-C.: Query type classification for web document retrieval. In: SIGIR 2003, pp. 64–71 (2003)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML 2001, pp. 282–289 (2001)
Li, X., Wang, Y.-Y., Acero, A.: Learning query intent from regularized click graphs. In: SIGIR 2008, pp. 339–346 (2008)
Sahami, M., Heilman, T.D.: A web-based kernel function for measuring the similarity of short text snippets. In: WWW 2006, pp. 377–386 (2006)
Shen, D., Sun, J.-T., Yang, Q., Chen, Z.: Building bridges for web query classification. In: SIGIR 2006, pp. 131–138 (2006)
Sutton, C.A., Rohanimanesh, K., McCallum, A.: Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. In: ICML 2004 (2004)
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Hu, D.H., Shen, D., Sun, JT., Yang, Q., Chen, Z. (2009). Context-Aware Online Commercial Intention Detection. In: Zhou, ZH., Washio, T. (eds) Advances in Machine Learning. ACML 2009. Lecture Notes in Computer Science(), vol 5828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05224-8_12
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DOI: https://doi.org/10.1007/978-3-642-05224-8_12
Publisher Name: Springer, Berlin, Heidelberg
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