Abstract
Personalized Web search systems have been developed to tailor Web search to users’ needs based on their interests and preferences. A novel Normal Distribution Re-Weighting (NDRW) approach is proposed in this paper, which identifies and re-weights significant terms in vector-based personalization models in order to improve the personalization process. Machine learning approaches will be used to train the algorithm and discover optimal settings for the NDRW parameters. Correlating these parameters to features of the personalization model will allow this re-weighting process to become automatic.
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Liu, H., Hoeber, O. (2011). Normal Distribution Re-Weighting for Personalized Web Search. In: Butz, C., Lingras, P. (eds) Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science(), vol 6657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_34
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DOI: https://doi.org/10.1007/978-3-642-21043-3_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21042-6
Online ISBN: 978-3-642-21043-3
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