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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
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Feature-based recommendations for one-to-one marketing.

Sung-Shun Weng1, Mei-Ju Liu1

  • 1Department of Information Management, Fu-Jen Catholic University, HsinChuang City, Taipei 242, Taiwan, ROC.

Expert Systems with Applications
|April 15, 2020
PubMed
Summary

This study introduces a new recommendation system that analyzes customer purchasing behavior and product features. It effectively recommends new or rarely purchased items by understanding customer interest profiles, overcoming limitations of traditional methods.

Keywords:
ClusteringOne-to-one marketingPersonalizationRecommendation system

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Area of Science:

  • Information Science
  • Computer Science
  • Marketing Science

Background:

  • Traditional recommendation systems struggle with time-sensitive products (popular, seasonal) and new or rarely purchased items due to limited purchase/rating data.
  • Market basket analysis and collaborative filtering methods fail to recommend products lacking sufficient customer interaction history.
  • Low ratings for infrequently bought items (e.g., furniture, appliances) reduce their visibility in existing recommendation engines.

Purpose of the Study:

  • To develop an improved recommendation system capable of suggesting new and infrequently purchased products.
  • To analyze customer purchasing behaviors by integrating transaction records with product feature databases.
  • To create customer interest profiles based on preferences for specific product features.

Main Methods:

  • Customer purchasing behaviors are analyzed using transaction records and product feature databases.
  • Customer preferences for product features are identified to construct customer interest profiles.
  • A two-stage clustering technique is employed to identify customer segments with similar interests.

Main Results:

  • The proposed system successfully recommends new and rarely purchased products by matching them to customer interest profiles.
  • Customer interest profiles provide explainability for recommendation results.
  • Analysis of feature preferences offers insights for product development and targeted marketing strategies.

Conclusions:

  • This research presents a novel approach to recommendation systems that overcomes the cold-start problem for new and unpopular products.
  • The method enhances recommendation accuracy by focusing on product features and customer preferences.
  • Insights gained can inform product development and enable more profitable one-to-one marketing strategies.