An associative classification-based recommendation system for personalization in B2C e-commerce applications

This chapter presents an associative classification-based recommendation system to support online customer decision-making when facing a huge amount of choices. Recommendation systems have been recently introduced to e-commerce sites in order to solve the information overload and mass confusion problem. This chapter applies knowledge discovery techniques to overcome the drawback of conventional approaches to recommendation systems. The framework of the associative classification-based recommendation system has been addressed in this chapter. The system analysis, design, and implementation issues in an Internet programming environment are also presented. Taking the advantage of accumulative knowledge from historical data, the efficiency and effectiveness of B2C e-commerce applications are improved.

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

Industrial Management & Data Systems ◽

10.1108/imds-02-2017-0057 ◽

2018 ◽

Vol 118 (1) ◽

pp. 188-203 ◽

Cited By ~ 4

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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Maximizing customer satisfaction through an online recommendation system: A novel associative classification model

Decision Support Systems ◽

10.1016/j.dss.2009.06.006 ◽

2010 ◽

Vol 48 (3) ◽

pp. 470-479 ◽

Cited By ~ 79

Author(s):

Yuanchun Jiang ◽

Jennifer Shang ◽

Yezheng Liu

Keyword(s):

Customer Satisfaction ◽

Recommendation System ◽

Classification Model ◽

Associative Classification ◽

System A

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Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207330 ◽

2020 ◽

pp. 120-127

Author(s):

Htay Htay Win ◽

Aye Thida Myint ◽

Mi Cho Cho

Keyword(s):

Social Network ◽

Dimensionality Reduction ◽

Correspondence Analysis ◽

Recommendation System ◽

Research Paper ◽

Topic Modelling ◽

Research Papers ◽

The Right ◽

Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

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