Abdallah Namoun, Abdullah M. Alshanqiti, Ezzat Chamudi, Mohammed Ayman Rahmon
{"title":"Web Design Scraping: Enabling Factors, Opportunities and Research Directions","authors":"Abdallah Namoun, Abdullah M. Alshanqiti, Ezzat Chamudi, Mohammed Ayman Rahmon","doi":"10.1109/ICITEE49829.2020.9271770","DOIUrl":null,"url":null,"abstract":"The number of online users accessing websites and consuming online services via their desktop and mobile browsers is on a constant rise. This paper coins and discusses the concept of web design scraping, which primarily promotes the idea of extracting, understanding, and modeling website components and characteristics and thereby inferring the meaning of the web design. Moreover, web design scraping emphasizes the role of state-of-the-art computing technologies and approaches, especially those powered by machine learning, in consolidating the understanding and intelligent construction and customization of website interfaces. Moreover, this paper discusses several enabling factors for web design scraping and recommends four research directions. The most notable research trends focus on the prediction of user satisfaction towards personalized web designs and the smart revamping of websites automatically by applying machine learning techniques that learn from the interaction history, preferences, and habits of online users.","PeriodicalId":245013,"journal":{"name":"2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICITEE49829.2020.9271770","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The number of online users accessing websites and consuming online services via their desktop and mobile browsers is on a constant rise. This paper coins and discusses the concept of web design scraping, which primarily promotes the idea of extracting, understanding, and modeling website components and characteristics and thereby inferring the meaning of the web design. Moreover, web design scraping emphasizes the role of state-of-the-art computing technologies and approaches, especially those powered by machine learning, in consolidating the understanding and intelligent construction and customization of website interfaces. Moreover, this paper discusses several enabling factors for web design scraping and recommends four research directions. The most notable research trends focus on the prediction of user satisfaction towards personalized web designs and the smart revamping of websites automatically by applying machine learning techniques that learn from the interaction history, preferences, and habits of online users.