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Automatic Clustering of User Behaviour Profiles for web Recommendation System

Intelligent Automation & Soft Computing 2023年第3期35卷 3365-3384页

作者： S.Sadesh Osamah Ibrahim Khalaf Mohammad Shorfuzzaman Abdulmajeed Alsufyani K.Sangeetha Mueen Uddin Department of Computer Science and Engineering Velalar College of Engineering and TechnologyErode638012Tamil NaduIndia Al-Nahrain Nanorenewable Energy Research Center Al-Nahrain UniversityBaghdad64074Iraq Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia School of Digital Science University Brunei DarussalamJln Tungku LinkGadong BE1410Brunei Darussalam

web usage mining,content mining,and structure mining comprise the web mining ***-Page Recommendation(WPR)development by incor-porating Data mining Techniques(DMT)did not include end-users with improved performance in the obtainedﬁltering *** cluster user proﬁle-based clustering process is delayed when it has a low precision *** Chain Monte Carlo-Dynamic Clustering(MC2-DC)is based on the User Behavior Proﬁle(UBP)model group’s similar user behavior on a dynamic update of *** Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate ***’s Filtering Framework(HFF)is designed toﬁlter user data based on personalised information on automatically updated UBP through the Search Engine(SE).The Hamilton Filtered Regime Switching User Query Probability(HFRSUQP)works forward the updated UBP for easy and accurateﬁltering of users’interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution(PURFR-GD)has been developed to user rank results in a web mining ***-GD decreases the delay time in the end-to-end workﬂow for SE personalization in various meth-ods by using the Gaussian Distribution Function(GDF).The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood(ML-L)increases to 15.28%of User Personalized Information Search Retrieval Rate(UPISRT).For feature ranking,the PURFR-GD model deﬁnes higher Classiﬁcation Accuracy(CA)and Precision Ratio(PR)while uti-lising minimum Execution Time(ET).Furthermore,UPISRT's ranking perfor-mance has improved by 20%.

关键词： Data mining web mining process search engine web-page recommendation accuracy

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