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Neural Networks in Big Data and Web Search

Author

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  • Will Serrano

    (Intelligent Systems and Networks Group, Imperial College London, SW7 2AZ London, UK)

Abstract

As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of crawling and indexing information within the enormous size and scale of the Internet, e-commerce customers and general Web users should not stay confident that the products suggested or results displayed are either complete or relevant to their search aspirations due to the commercial background of the search service. The economic priority of Web-related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers. On the other hand, web search engine and recommender system revenue is obtained from advertisements and pay-per-click. The essential user experience is the self-assurance that the results provided are relevant and exhaustive. This survey paper presents a review of neural networks in Big Data and web search that covers web search engines, ranking algorithms, citation analysis and recommender systems. The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications. Finally, the random neural network is presented with its practical applications to reasoning approaches for knowledge extraction.

Suggested Citation

  • Will Serrano, 2018. "Neural Networks in Big Data and Web Search," Data, MDPI, vol. 4(1), pages 1-41, December.
    • Handle: RePEc:gam:jdataj:v:4:y:2018:i:1:p:7-:d:194034
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    References listed on IDEAS

    as
    1. Mike Thelwall, 2008. "Quantitative comparisons of search engine results," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(11), pages 1702-1710, September.
    2. Anne-Wil Harzing & Satu Alakangas, 2016. "Google Scholar, Scopus and the Web of Science: a longitudinal and cross-disciplinary comparison," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 787-804, February.
    3. Jiyin He & Edgar Meij & Maarten de Rijke, 2011. "Result diversification based on query‐specific cluster ranking," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(3), pages 550-571, March.
    4. Jiyin He & Edgar Meij & Maarten de Rijke, 2011. "Result diversification based on query-specific cluster ranking," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(3), pages 550-571, March.
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