Normal distribution re-weighting for personalized web search

Authors:
Hanze Liu;Orland Hoeber
Affiliations:
Department of Computer Science, Memorial University, St. John's, N.L, Canada;Department of Computer Science, Memorial University, St. John's, N.L, Canada
Venue:
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Year:
2011

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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.