A cost-sensitive technique for positive-example learning supporting content-based product recommendations in B-to-C e-commerce

  • Authors:

  • Yen-Hsien Lee;Paul Jen-Hwa Hu;Tsang-Hsiang Cheng;Ya-Fang Hsieh

  • Affiliations:

  • Department of Management Information Systems, College of Management, National Chiayi University, Taiwan;Department of Operations and Information Systems, David Eccles School of Business, University of Utah, United States;Department of Business Administration, College of Management, Southern Taiwan University, Taiwan;Technical Support, ASML, Taiwan

  • Venue:
  • Decision Support Systems
  • Year:
  • 2012

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Abstract

Existing supervised learning techniques are able to support product recommendations in business-to-consumer e-commerce but become ineffective in scenarios characterized by single-class learning, such as a training sample that consists of some examples pertaining to only one outcome class (positive or negative). To address such challenges, we develop a COst-sensitive Learning-based Positive Example Learning (COLPEL) technique, which constructs an automated classifier from a training sample comprised of positive examples and a much larger number of unlabeled examples. The proposed technique incorporates cost-proportionate rejection sampling to derive, from unlabeled examples, a subset that is likely to feature negative examples in the training sample. Our technique follows a committee machine approach and thereby constructs a set of classifiers that make joint product recommendations while mitigating the potential biases common to the use of a single classifier. We evaluate the proposed method with customers' book ratings collected from Amazon.com and include two prevalent techniques for benchmark purposes; namely, positive naive Bayes and positive example-based learning. According to our results, the proposed COLPEL technique outperforms both benchmarks, as measured by accuracy and positive and negative F1 scores.