Next Article in Journal
Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy
Next Article in Special Issue
From a Smoking Gun to Spent Fuel: Principled Subsampling Methods for Building Big Language Data Corpora from Monitor Corpora
Previous Article in Journal
Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response
Previous Article in Special Issue
CRC806-KB: A Semantic MediaWiki Based Collaborative Knowledge Base for an Interdisciplinary Research Project
Article Menu
Issue 1 (March) cover image

Export Article

Open AccessReview

Neural Networks in Big Data and Web Search

Intelligent Systems and Networks Group, Imperial College London, SW7 2AZ London, UK
This article is an extended version of the paper “Will Serrano, The Random Neural Network and Web Search: Survey Paper” presented in Intelligent Systems Conference (2018), 6–7 September 2018, London, UK.
Received: 4 November 2018 / Revised: 24 December 2018 / Accepted: 24 December 2018 / Published: 30 December 2018
(This article belongs to the Special Issue Semantics in the Deep: Semantic Analytics for Big Data)
PDF [3151 KB, uploaded 30 December 2018]


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. View Full-Text
Keywords: big data; neural networks; ranking algorithms; web search; deep learning; recommender systems big data; neural networks; ranking algorithms; web search; deep learning; recommender systems

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Serrano, W. Neural Networks in Big Data and Web Search. Data 2019, 4, 7.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics



[Return to top]
Data EISSN 2306-5729 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top