A rough set-based association rule approach for a recommendation system for online consumers

doi:10.1016/j.ipm.2016.05.003

Information Processing & Management

Volume 52, Issue 6, November 2016, Pages 1142-1160

https://doi.org/10.1016/j.ipm.2016.05.003 Get rights and content

Highlights

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It is increased the importance of understanding online consumers’ purchase behaviors.
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Recommendation systems are decision aids that analyze customer's prior online behavior.
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This study proposes a rough set-based association rule approach.
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It is developed from ordinal data scale processing for customer’s preference analysis.
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We find some patterns and rules for e-commerce platform recommendations.

Abstract

Increasing use of the Internet gives consumers an evolving medium for the purchase of products and services and this use means that the determinants for online consumers’ purchasing behaviors are more important. Recommendation systems are decision aids that analyze a customer's prior online purchasing behavior and current product information to find matches for the customer's preferences. Some studies have also shown that sellers can use specifically designed techniques to alter consumer behavior. This study proposes a rough set based association rule approach for customer preference analysis that is developed from analytic hierarchy process (AHP) ordinal data scale processing. The proposed analysis approach generates rough set attribute functions, association rules and their modification mechanism. It also determines patterns and rules for e-commerce platforms and product category recommendations and it determines possible behavioral changes for online consumers.

Graphical abstract

Section snippets

Research background

Most online businesses that are involved in the sales of products/services, such as commercial websites, are aware of the need to acquire knowledge about their online consumers. However, knowledge about online consumers, though available, is not accessible, so it is critical to analyze all of the available knowledge if online users in search for information, products, or services and then highlight potential product promotions and marketing alternatives from online firms. In this regard,

Recommendation systems

Since the development of the first recommendation system by Goldberg, Nichols, Oki, and Terry (1992), various recommendation systems and related technologies, such as CBF and CF, have been reported (Herlocker et al., 2004, Zenebe and Norcio, 2009). Of these, user-based CF is considered to be the most successful recommendation technique and is successfully used by many e-commerce systems, such as Amazon.com and Dell.com (Konstan et al., 1997). CF elicits superior preference information from the

Methodology – the rough set-based association rule approach

In terms of developing rough set and association rules for a recommendation system, this study proposes a rough set based association rule approach that is developed from ordinal data scale processing for customer preference analysis. The proposed analysis approach generates rough set attribute functions, association rules and their modification mechanism. The steps for developing the algorithms that are involved in the proposed approach are as follows:

Step 1: Data processing — create a data

Proposing recommendation system for online shopping platforms

In the regard of experimental design on proposing recommendation system, by using the AHP rough set-based association rule approach, this study implements a recommendation system for Internet shopping portals by determining online consumer purchase preferences using a questionnaire survey. A total of 850 questionnaires were distributed and 720 questionnaires were returned, including 707 effective questionnaires. Nominal and ordinal scale questions were used. The questionnaire contained four

Behavioral change due to recommendation systems

In the regard of experimental design on changing customer behavior, this study also determines whether consumers accept the recommendation results that are generated by the proposed approach and whether they then change their purchase behaviors. Using the same subjects as the previous numerical example, the conditions for acceptance of the recommendations on an Internet portal and product category are determined. A total of 850 questionnaires were sent and 698 questionnaires were returned,

Conclusion

As business assets, consumers play a vital role in marketing. Most of the parties involved in product sales, such as commercial web sites, retailers and channels, are aware of the need for businesses to acquire and share better customer knowledge. However, the opportunities are limited because knowledge about customers is available but not accessible, and there is little possibility of fully analyzing all of the data that must be collected. The effective processing and use of data has become

Acknowledgements

This research was funded by the Ministry of Science and Technology, Taiwan, Republic of China (MOST 103-2410-H-032-043-MY3).

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A rough set-based association rule approach for a recommendation system for online consumers

Highlights

Abstract

Graphical abstract

Section snippets

Research background

Recommendation systems

Methodology – the rough set-based association rule approach

Proposing recommendation system for online shopping platforms

Behavioral change due to recommendation systems

Conclusion

Acknowledgements

Knowledge-Based Systems

Expert Systems with Applications

Decision Support Systems

Journal of Economic Psychology

European Journal of Operational Research

Expert Systems with Applications

Decision Support Systems

Data & Knowledge Engineering

Decision Support Systems

Information Processing & Management

Knowledge-based Systems

Expert Systems with Applications

Expert Systems with Applications

Information Sciences

Knowledge-based Systems

Information Processing & Management

Information Sciences

Information Sciences

Information Sciences

Information Processing & Management

Information Sciences

Decision Support Systems

Expert Systems with Applications

Decision Support Systems