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

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 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.