Automatic Clustering of User Behaviour Profiles for Web Recommendation System  

作　　者：S.Sadesh Osamah Ibrahim Khalaf Mohammad Shorfuzzaman Abdulmajeed Alsufyani K.Sangeetha Mueen Uddin 

机构地区：[1]Department of Computer Science and Engineering,Velalar College of Engineering and Technology,Erode,638012,Tamil Nadu,India [2]Al-Nahrain Nanorenewable Energy Research Center,Al-Nahrain University,Baghdad,64074,Iraq [3]Department of Computer Science,College of Computers and Information Technology,Taif University,P.O.Box 11099,Taif 21944,Saudi Arabia [4]Department of Computer Science and Engineering,Kongu Engineering College,Perundurai,638060,Tamil Nadu,India [5]School of Digital Science,University Brunei Darussalam,Jln Tungku Link,Gadong BE1410,Brunei Darussalam

出　　处：《Intelligent Automation & Soft Computing》2023年第3期3365-3384,共20页智能自动化与软计算（英文）

基　　金：Supporting this study through Taif University Researchers Supporting Project number(TURSP-2020/115),Taif University,Taif,Saudi Arabia.

摘　　要：Web usage mining,content mining,and structure mining comprise the web mining process.Web-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved performance in the obtainedfiltering results.The cluster user profile-based clustering process is delayed when it has a low precision rate.Markov 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 UBP.The Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate clusters.Hamilton’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 process.PURFR-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 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]