A feature-pair-based associative classification approach to look-alike modeling for conversion-oriented user-targeting in tail campaigns

2011 ◽  
Author(s):  
Ashish Mangalampalli ◽  
Adwait Ratnaparkhi ◽  
Andrew O. Hatch ◽  
Abraham Bagherjeiran ◽  
Rajesh Parekh ◽  
...  
Keyword(s):  
Associative Classification ◽  
Classification Approach ◽  
Feature Pair ◽  
User Targeting

scholarly journals A New Classification Approach Based on Multiple Classification Rules

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhongmei Zhou
Keyword(s):  
Classification Rule ◽  
Classification Rules ◽  
Associative Classification ◽  
New Classification ◽  
Class Label ◽  
Rule Based ◽  
Classification Approach ◽  
Minimum Support ◽  
Multiple Classification ◽  
Rule Set

A good classifier can correctly predict new data for which the class label is unknown, so it is important to construct a high accuracy classifier. Hence, classification techniques are much useful in ubiquitous computing. Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when the minimum support is set to be low. It is difficult to select a high quality rule set for classification. Second, the accuracy of associative classification depends on the setting of the minimum support and the minimum confidence. In comparison with associative classification, some improved traditional rule-based classification approaches often produce a classification rule set that plays an important role in prediction. Thus, some improved traditional rule-based classification approaches not only achieve better efficiency than associative classification but also get higher accuracy. In this paper, we put forward a new classification approach called CMR (classification based on multiple classification rules). CMR combines the advantages of both associative classification and rule-based classification. Our experimental results show that CMR gets higher accuracy than some traditional rule-based classification methods.

scholarly journals A FUZZY ASSOCIATIVE CLASSIFICATION APPROACH FOR RECOMMENDER SYSTEMS

2012 ◽  
Vol 20 (04) ◽  
pp. 579-617 ◽  
Author(s):  
JOEL PINHO LUCAS ◽  
ANNE LAURENT ◽  
MARÍA N. MORENO ◽  
MAGUELONNE TEISSEIRE
Keyword(s):  
Data Mining ◽  
Recommender Systems ◽  
Real Data ◽  
Associative Classification ◽  
Data Mining Technique ◽  
Classification Approach ◽  
Mining Technique ◽  
Joint Method ◽  
Hybrid Methodology

Despite the existence of different methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by means of an alternative data mining technique called classification based on association, which uses association rules in a prediction perspective. In this work we propose a hybrid methodology for recommender systems, which uses collaborative filtering and content-based approaches in a joint method taking advantage from the strengths of both approaches. Moreover, we also employ fuzzy logic to enhance recommendations' quality and effectiveness. In order to analyze the behavior of the techniques used in our methodology, we accomplished a case study using real data gathered from two recommender systems. Results revealed that such techniques can be applied effectively in recommender systems, minimizing the effects typical drawbacks they present.

A new recommendation system on the basis of consumer initiative decision based on an associative classification approach

2018 ◽  
Vol 118 (1) ◽  
pp. 188-203 ◽  
Author(s):  
Chengxin Yin ◽  
Yan Guo ◽  
Jianguo Yang ◽  
Xiaoting Ren
Keyword(s):  
Experimental Study ◽  
Customer Satisfaction ◽  
Design Methodology ◽  
Recommendation System ◽  
Classification Method ◽  
Personalized Recommendation ◽  
Associative Classification ◽  
Content Type ◽  
Classification Approach ◽  
Potential Buyer

Purpose The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system. Design/methodology/approach By employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop. Findings The results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision. Originality/value Conventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.

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