Abstract
User click behaviors reflect his preference in Web search processing objectively, and it is very important to give a proper interpretation of user click for improving search results. Previous click models explore the relationship between user examines and latent clicks web document obtained by search result page via multiple-click model, such as the independent click model(ICM) or the dependent click model(DCM),which the examining-next probability only depends on the current click. However, user examination on a search result page is a continuous and relevant procedure. In this paper, we attempt to explore the historical clicked data using a probability click tracking model(PCTM). In our approach, the examine-next probability is decided by the click variables of each clicked result. We evaluate the proposed model on a real-world data set obtained from a commercial search engine. The experiment results illustrate that PCTM can achieve the competitive performance compared with the existing click models under standard metrics.
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Yang, Y., Shu, X., Liu, W. (2010). A Probability Click Tracking Model Analysis of Web Search Results. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_40
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DOI: https://doi.org/10.1007/978-3-642-17537-4_40
Publisher Name: Springer, Berlin, Heidelberg
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