scholarly journals User Behavior Identification and Personalized Recommendation Based on Web Data Mining

2021 ◽  
Vol 15 ◽  
pp. 643-650
Author(s):  
Ya Wang
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
User Behavior ◽  
Matrix Decomposition ◽  
Personalized Recommendation ◽  
K Nearest Neighbor ◽  
Web Data ◽  
Web Data Mining ◽  
User Behavior Analysis ◽  
Model Based

A good understanding of user behavior and consumption preferences can provide support for website operators to improve their service quality. However, the existing personalized recommendation systems generally have problems such as low Web data mining efficiency, low degree of automated recommendation, and low durability. Targeting at these unsolved issues, this paper mainly carries out the following works: Firstly, the authors established a user behavior identification and personalized recommendation model based on Web data mining, it gave the user behavior analysis process based on Web data mining, improved the traditional k-means algorithm, and gave the detailed execution steps of the improved algorithm; moreover, it also elaborated on the K nearest neighbor model based on user scoring information, the score matrix decomposition method, and the personalized recommendation method for network users. At last, experimental results verified the effectiveness of the constructed model.

Improvement of Web Data Mining Method and its Application in Personalized Recommendation

2014 ◽  
Vol 13 (2) ◽  
pp. 333-339 ◽  
Author(s):  
Yao Chunlong ◽  
Sun Cuicui . ◽  
Fan Fenglong . ◽  
Shen Lan .
Keyword(s):  
Data Mining ◽  
Personalized Recommendation ◽  
Mining Method ◽  
Web Data ◽  
Web Data Mining ◽  
Data Mining Method

Research of Web Data Mining in Personalized Recommendation Service

2012 ◽  
Vol 546-547 ◽  
pp. 429-434
Author(s):  
Ge Wang ◽  
Chan Juan Liu ◽  
Peng Bo Pu
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Web Site ◽  
Personalized Recommendation ◽  
Web Data ◽  
Personalized Service ◽  
Web Data Mining ◽  
Knowledge Mining ◽  
Information Category

To offer personalized recommendation service to web users, it adopts improved FP-Growth algorithm, introduces its implementation methods and directly applies it to the recessive knowledge mining of web site information category in details. Via mining the website data and analysing association rules, useful relavant knowledge is obtained, tendancy of website visiting can be prediced and personalized service will be prescribed which make website more friendly and satisfactory.

A user behavior prediction model based on parallel neural network and k-nearest neighbor algorithms

2017 ◽  
Vol 20 (2) ◽  
pp. 1703-1715 ◽  
Author(s):  
Gaowei Xu ◽  
Carl Shen ◽  
Min Liu ◽  
Feng Zhang ◽  
Weiming Shen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Nearest Neighbor ◽  
User Behavior ◽  
Behavior Prediction ◽  
K Nearest Neighbor ◽  
Model Based

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

Sign in / Sign up

Export Citation Format

Share Document