Application and Research of Data Mining in Micro Course Platform Construction

2014 ◽  
Vol 962-965 ◽  
pp. 3003-3006
Author(s):  
Bao Xian Jia
Keyword(s):  
Data Mining ◽  
Key Words ◽  
Personalized Recommendation ◽  
Basic Information ◽  
Personalized Service ◽  
Learning Content ◽  
Content Recommendation ◽  
Matching Degree ◽  
Access Information ◽  
Mining Pattern

Data mining can be used to make modeling for individual learner's usage record, combining with learner's basic information to make analysis of his habits, personal preferences to provide personalized service for the learner. At the same time by collecting and counting learners’ recent access information in micro course platform to analyse the learning content, compare and match with mining pattern, and to sort according to the matching degree, forecasting the most possible knowledge for the learner in the next step, attaching sorting result to the end of the learner’s requested page, for the learning content recommendation consequently, etc. Paper mainly introduced the specific application of data mining in micro course platform BBS. Key words: data mining, micro courses, personalized recommendation

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 FLASH OF CONSTRUCTION SCHEMES COLLECTIVE IN THE CLASSROOM INVOLVING THE FIELD STRUCTURAL MOLECULAR

2015 ◽  
Author(s):  
Marcelo Gouveia Nascimento ◽  
◽  
Gabriel Nicolas Garcia Alves ◽  
Marco Antonio Bueno Filho ◽  
Rodrigo Luiz Oliveira Rodrigues Cunha ◽  
...  
Keyword(s):  
College Students ◽  
Liquid Chromatography ◽  
Key Words ◽  
Textual Analysis ◽  
Molecular Level ◽  
Chemical Bonds ◽  
Bachelor Degree ◽  
Access Information ◽  
Conceptual Relationships ◽  
Collective Construction

This study aims to access information about how college students collectively build action schemes in structural molecular level. The research had two situations presented to six students and Bachelor Degree in Chemistry from the Federal University of ABC involving content on Liquid Chromatography. The speeches of students organized in groups were recorded in audiovisual and subjected to Textual Analysis Discourse and grounded in the theory of Conceptual Fields (Vergnaud, 1996). The results were assessed, evidence of the collective construction of a scheme characterized by relevant conceptual relationships at the molecular structural field, but incomplete. Key words: theory of conceptual fields, collective schemes, chemical bonds.

scholarly journals A Framework for Data Mining Pattern Management

2004 ◽  
pp. 87-98 ◽  
Author(s):  
Barbara Catania ◽  
Anna Maddalena ◽  
Maurizio Mazza ◽  
Elisa Bertino ◽  
Stefano Rizzi
Keyword(s):  
Data Mining ◽  
Mining Pattern

Personalized Content Recommendation Engine for Web Publishing Services Using Textmining and Predictive Analytics

2017 ◽  
pp. 113-124
Author(s):  
Başar Öztayşi ◽  
Ahmet Tezcan Tekin ◽  
Cansu Özdikicioğlu ◽  
Kerim Caner Tümkaya
Keyword(s):  
Recommendation System ◽  
Predictive Analytics ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Web Contents ◽  
Interest Level ◽  
History Of ◽  
Content Recommendation ◽  
Publishing Services ◽  
Web Publishing

Recommendation systems have become very important especially for internet based business such as e-commerce and web publishing. While content based filtering and collaborative filtering are most commonly used groups in recommendation systems there are still researches for new approaches. In this study, a personalized recommendation system based on text mining and predictive analytics is proposed for a real world web publishing company. The approach given in this chapter first preprocesses existing web contents, integrate the structured data with history of a specific user and create an extended TDM for the user. Then this data is used for prediction of the users interest in new content. In order to reach that point, SVM, K-NN and Naïve Bayesian methods are used. Finally, the best performing method is used for determining the interest level of the user in a new content. Based on the forecasted interest levels the system recommends among the alternatives.

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 on Intelligent Recommendation Method and its Application on Internet Bookstore

2010 ◽  
Vol 121-122 ◽  
pp. 447-452
Author(s):  
Qing Zhang Chen ◽  
Yu Jie Pei ◽  
Yan Jin ◽  
Li Yan Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
The Internet

As the current personalized recommendation systems of Internet bookstore are limited too much in function, this paper build a kind of Internet bookstore recommendation system based on “Strategic Data Mining”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. Then the method clusters the customer and type of book, and gives some strategies of personalized recommendation. Internet bookstore recommendation system is implemented with ASP.NET in this article. The experimental results indicate that the Internet bookstore recommendation system is feasible.

scholarly journals A M-Learning Content Recommendation Service by Exploiting Mobile Social Interactions

2014 ◽  
Vol 7 (3) ◽  
pp. 221-230 ◽  
Author(s):  
Han-Chieh Chao ◽  
Chin-Feng Lai ◽  
Shih-Yeh Chen ◽  
Yueh-Min Huang
Keyword(s):  
Social Interactions ◽  
Learning Content ◽  
Content Recommendation
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