Existing supervised learning techniques are able to support product recommendations in business-toconsumer 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 naïve 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