Open AccessCombination of data mining and artificial intelligence algorithms for efficient web page recommendation
Author(s) -
Manish Sharma,
Vijay Kumar Singh,
Priyanka Surendran,
Bindhya Thomas,
Shaminder Singh Sohi,
Rajesh Kumar Dubey
Publication year - 2022
Publication title -
international journal of health sciences (ijhs) (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2550-6978
pISSN - 2550-696X
DOI - 10.53730/ijhs.v6ns3.6076
Subject(s) - computer science , web mining , information retrieval , web page , web search query , web search engine , web query classification , association rule learning , search engine , static web page , scalability , data web , data mining , world wide web , web analytics , metasearch engine , web intelligence , web navigation , web modeling , database
Due to the obvious unstable increase in information, the web is saturated with data, which makes the data search a complicated task. Existing web-based recommendation systems include shortcomings such as a lack of capability as well as scalability when dealing with online data, and blockages created by traffic while utilising the website during peak hours. Web recommendation systems help consumers find the right content and make the information search process easier. Web usage mining is regarded as the primary source for web recommendation, and it is used in conjunction with association rule mining and the C4.5 algorithm to recommend online pages to the user. The Google search engine has been widely enhanced the likelihood on the system's suggested structure. A web log is created when a user enters a search query into a search engine. This query would be compared to the web logs by the proposed system. The associate rule mining technique helps in matching the user's search query to the online log. The C4.5 algorithm is linked to a priority based on reviews, which obviously ranks the search based on priority for greater validation result.