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Traditional search engines usually provide same set of output links to different users in response to same search query, despite, personalized search requirements vary among users. Moreover, gigantic and ever-growing nature of web leads to incomplete indexing even by most of popular search engines. In this paper, we address the issues of personalized search requirements by proposing a cloud architecture of a Meta search tool implemented through Hadoop framework. The extensive experimental evaluation clearly shows that the proposed Meta search framework can easily outperform popular search engines in terms of personalized search precision.

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An Intelligent Approach for Web Search Personalization Using Machine Learning and Big Data Analytics

  • 会议论文

  • 发表日期：

    2025年

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  • Dheeraj Malhotra,
  • Midhun Chakkrarvarthy

摘要

Traditional search engines usually provide same set of output links to different users in response to same search query, despite, personalized search requirements vary among users. Moreover, gigantic and ever-growing nature of web leads to incomplete indexing even by most of popular search engines. In this paper, we address the issues of personalized search requirements by proposing a cloud architecture of a Meta search tool implemented through Hadoop framework. The extensive experimental evaluation clearly shows that the proposed Meta search framework can easily outperform popular search engines in terms of personalized search precision.