Inferential language modeling for selective Web search personalization and contextualization

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationICACTE 2010 - 2010 3rd International Conference on Advanced Computer Theory and Engineering, Proceedings
PagesV1540-V1544
Volume1
Publication statusPublished - 2010

Publication series

Name
Volume1

Conference

Title2010 3rd International Conference on Advanced Computer Theory and Engineering, ICACTE 2010
PlaceChina
CityChengdu
Period20 - 22 August 2010

Abstract

Personalized Web search systems have been explored to alleviate the problem of information overload by keeping track of a user's specific information retrieval (IR) preferences, and then pushing information to the user according to their preferences maintained in a user profile. Nevertheless, personalization and contextualization is always associated with a computational cost. Therefore, it is more advantageous for a personalized Web search system to evaluate the necessity of personalization for a query before invoking the personalization mechanism. Unfortunately, most of the existing personalized Web search approaches only blindly personalize users' queries without considering the characteristic of the queries or the searchers who issue those queries. The main contributions of our research work presented in this paper are two fold. First, a novel selective Web search personalization and contextualization method is developed to enhance the effectiveness of personalized Web search. Second, an inferential language model which can take into account the semantic and contextual information associated with a Web search scenario is developed to enhance the selective personalization and contextualization process. The results of our initial experiment show that the proposed selective personalization and contextualization method underpinned by inferential language modeling significantly outperforms a baseline method developed based on syntactic click entropy. To the best of our knowledge, this is the first inferential language modeling approach that has been successfully applied to Web search personalization and contextualization. © 2010 IEEE.

Research Area(s)

  • Inferential language model, Personalization and contextualization, Search context, Text mining, Web search

Citation Format(s)

Inferential language modeling for selective Web search personalization and contextualization. / LAU, Raymond Y. K.
ICACTE 2010 - 2010 3rd International Conference on Advanced Computer Theory and Engineering, Proceedings. Vol. 1 2010. p. V1540-V1544 5578957.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review