Recommendations of Compatible Accessories in e-Commerce

We address the problem of learning how compatible two products are. Assessing compatibility is challenging because the meaning of compatibility changes depending on product categories. In this study, we leverage domain experts' knowledge to generate labels and datasets. Next, we engineer 58 different features from product titles and product descriptions. We experiment with both tree-based and deep learning classifiers using different sets of features to capture compatibility patterns across four product categories. Although the performance does not show consistent pattern across all categories, the precision and recall of the best algorithm from most categories are above 90%. In addition, we find that the performance of classifiers are in general satisfactory. Based on human validation, few best-performing classifiers demonstrate better performance than labels generated from domain experts.