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.
Highlights
The Internet has enabled the direct connection between users and information
[59]specific within tourism domain domain web search based on search address, language, city, and key terms; specific tourism webqueries search queries based on time, searchIPtime, IP address, language, city, and key the study clicking and re-clicking on the search engines is the mostisrepetitive terms; theshows study that shows that clicking and re-clicking on the searchhomepage engines homepage the most and iterative task of webtask users, is most be included key term in search repetitive and iterative of the webcity users, the likely city istomost likely toasbea included as the a key termqueries, in the search queries, followed by the hotel where, web users normally select the results that show the city home web page
The algorithm first submits a query to the different web search engines where the top 200 web pages of every web search engine are retrieved; it ranks the pages in relation to their likeness to the web user information requirements; the model is built on the vector space method for query-document matching and ranking using TF-IDF
Summary
The Internet has enabled the direct connection between users and information. This has fundamentally changed how businesses operate, including the travel industry and e-commerce. Web search engines and recommender systems were developed as interfaces between users and the Internet to address the need for searching precise data and items. They provide a straight link between web users with the pursued products or wanted information, any web search result list or suggestion will be biased due profitable economic or personal interests along with by the users’ own. Neural networks and artificial intelligence have been applied to web searching in result ranking and relevance as a method to learn and adapt to variable user interests.
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