Product Bundle Identification using Semi-Supervised Learning

Many sellers on e-commerce platforms offer buyers product bundles, which package together two or more different items. The identification of such bundles is a necessary step to support a variety of related services, from recommendation to dynamic pricing. In this work, we present a comprehensive study of bundle identification on a large e-commerce website. Our analysis of bundle compared to non-bundle listed items reveals several key differentiating characteristics, spanning the listing's title, image, and attributes. Following, we experiment with a multi-modal classifier, which takes advantage of these characteristics as features. Our analysis also shows that a bundle indicator input by sellers tends to be highly noisy and carries only a weak signal. The bundle identification task therefore faces the challenge of having a small set of manually-labeled clean examples and a larger set of noisy-labeled examples, in conjunction with class imbalance due to the relative scarcity of bundles. Our experiments with basic supervised classifiers, using the manually-labeled and/or the noisy-labeled data for training, demonstrates only moderate performance. We therefore turn to a semi-supervised approach and propose GREED, a self-training ensemble-based algorithm with a greedy model selection. Our evaluation over two different meta-categories shows a superior performance of semi-supervised approaches for the bundle identification task, with GREED outperforming several semi-supervised alternatives. The combination of textual, image, and some metadata features is shown to yield the best performance, reaching an AUC of 0.89 and 0.92 for the two meta-categories, respectively.