A Study of Intelligent Recommendation System based on Naive Bayes Text Classification and Collaborative Filtering

2010 ◽  
Vol 41 (4) ◽  
pp. 227-249 ◽  
Author(s):  
Sang-Gi Lee ◽  
Byeong-Seop Lee ◽  
Byeong-Yong Bak ◽  
Hye-Kyong Hwang
2008 ◽  
Vol 4 (7) ◽  
pp. 600-605 ◽  
Author(s):  
Mohammed J. Bawaneh ◽  
Mahmud S. Alkoffash ◽  
Adnan I. Al Rabea

Author(s):  
Han-joon Kim

This chapter introduces two practical techniques for improving Naïve Bayes text classifiers that are widely used for text classification. The Naïve Bayes has been evaluated to be a practical text classification algorithm due to its simple classification model, reasonable classification accuracy, and easy update of classification model. Thus, many researchers have a strong incentive to improve the Naïve Bayes by combining it with other meta-learning approaches such as EM (Expectation Maximization) and Boosting. The EM approach is to combine the Naïve Bayes with the EM algorithm and the Boosting approach is to use the Naïve Bayes as a base classifier in the AdaBoost algorithm. For both approaches, a special uncertainty measure fit for Naïve Bayes learning is used. In the Naïve Bayes learning framework, these approaches are expected to be practical solutions to the problem of lack of training documents in text classification systems.


Author(s):  
I Made Agus Wirawan ◽  
I Wayan Bayu Diarsa

<p class="0abstractCxSpFirst">Although it has been a lot of research recommendation of the tourist attraction, there has been no research that discusses the recommendations of tour packages from a collection of travel in the past. Therefore, in this study it is important to conduct a related study 1) The development of a mobile recommendation system using the Hybrid Method. 2) Test system accuracy in providing tour package recommendations.</p><p class="0abstractCxSpLast">The study is using CBR stages in providing travel package recommendations from a collection of travel in the past. There are 4 stages of the process: Retrieve, Reuse, Revise, and Retain. In this study the main focus on the retrieve stage using the method hybrid method. The hybrid method of the mobile recommendation system is the combination of the Naive Bayes method, Bayes Theorem, and Dempster Shafer. Where Naive Bayes is used for calculating the probability of continuous criteria such as age and frequency of visits. The Bayes theorem is used for calculating the probability such as country, gender, and visiting purpose. To determine the mass value of the combination of evidence using the Dempster Shafer method. Based on system accuracy test, stated that the total system accuracy in giving recommendation is 95% consisting of 2 kinds of accuracy is 46% full accuracy and 49% of half accuracy. While the error rate of the system in providing tour package of 5%.</p>


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