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Comprehensive Analysis of Personalized Web Search Engines Through Information Retrieval Feedback System and User Profiling | SpringerLink
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Comprehensive Analysis of Personalized Web Search Engines Through Information Retrieval Feedback System and User Profiling

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Advanced Informatics for Computing Research (ICAICR 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 956))

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Abstract

Information retrieval with its feedback feature provides the way to bridge gap between user’s search queries and the documents returned by search engines. Recently, there has been a drift of personalization in Web search by many commercial and prominent search engines, where users receive different search results without considering relevancy of search query. Though many of the search engines are facilitating the features of personalized search results to provide the best user experiences of their search context. This paper provides composite review of research done for the personalization the web search as well as notified efforts has been done by web search engines to provide personalized results to users without compromising their privacy of search queries. Through the comparative analysis it has been identified the performance of key parameters like accuracy, efficiency and diversity of retrieved search result w.r.t. various user profiling and retrieval model techniques.

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Authors and Affiliations

  1. Charusat University, Changa, Gujarat, India

    Kamlesh Makvana, Jay Patel, Parth Shah & Amit Thakkar

Authors
  1. Kamlesh Makvana
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  2. Jay Patel
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  3. Parth Shah
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  4. Amit Thakkar
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Corresponding author

Correspondence to Kamlesh Makvana .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Maharshi Dayanand University, Rohtak, Haryana, India

    Ashish Kumar Luhach

  2. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh

  3. National Chung Cheng University, Minxiong Township, Chiayi County, Taiwan

    Pao-Ann Hsiung

  4. Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Kamarul Bin Ghazali Hawari

  5. Saint Mary’s University, Halifax, NS, Canada

    Pawan Lingras

  6. Department of Computer Science and Engineering, Jaypee University of Information Technology, Kandaghat, India

    Pradeep Kumar Singh

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Makvana, K., Patel, J., Shah, P., Thakkar, A. (2019). Comprehensive Analysis of Personalized Web Search Engines Through Information Retrieval Feedback System and User Profiling. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 956. Springer, Singapore. https://doi.org/10.1007/978-981-13-3143-5_14

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  • DOI: https://doi.org/10.1007/978-981-13-3143-5_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3142-8

  • Online ISBN: 978-981-13-3143-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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