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Combining User Interested Topic and Document Topic for Personalized Information Retrieval

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Big Data Analytics (BDA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8883))

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Abstract

Personalization aims to improve user’s searching experience by tailoring search results according to individual user’s interests. Typically, search engines employ two-level ranking strategy. Firstly, initial list of documents is prepared using a low-quality ranking function that is less computationally expensive. Secondly, initial list is re-ranked by machine learning algorithms which involve expensive computation. The proposed approach explores the second level of ranking strategy which exploits user information. In this approach, queries and search-result clicks are used to model the user interest profiles probabilistically. The user’s history provides the prior probability that a user searches for a topic which is independent of user query. The document topical features are combined with user specific information to determine whether a document satisfies user’s information need or not. The probability of relevance of each retrieved document for a query is computed by integrating user topic model and document topic model. Thus, documents are re-ranked according to the personalized score computed for each document. The proposed approach has been implemented and evaluated using real dataset similar to AOL search log dataset for personalization. Empirical results along with the theoretical foundations of the model confirm that the proposed approach shows promising results.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India

    K. Veningston & R. Shanmugalakshmi

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  1. K. Veningston
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  2. R. Shanmugalakshmi
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Editors and Affiliations

  1. International Institute of Information Technology - Bangalore, 26/C, Electronics City, Hosur Road, 560100, Bangalore, India

    Srinath Srinivasa

  2. IBM Research - India, 4 Block C, Institutional Area, Vasant Kunj, 110070, New Delhi, India

    Sameep Mehta

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Veningston, K., Shanmugalakshmi, R. (2014). Combining User Interested Topic and Document Topic for Personalized Information Retrieval. In: Srinivasa, S., Mehta, S. (eds) Big Data Analytics. BDA 2014. Lecture Notes in Computer Science, vol 8883. Springer, Cham. https://doi.org/10.1007/978-3-319-13820-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-13820-6_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13819-0

  • Online ISBN: 978-3-319-13820-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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