Semi-supervised Adversarial Learning for Complementary Item Recommendation
This addresses the challenge of making complementary recommendations in online marketplaces like auction sites where new items constantly appear, though it is incremental as it builds on existing adversarial learning techniques.
The paper tackles the problem of recommending complementary items for cold items (new items without co-purchase data) in e-commerce by leveraging both item side-information and labeled complementary pairs, resulting in a highly effective method as shown in experiments on three datasets.
Complementary item recommendations are a ubiquitous feature of modern e-commerce sites. Such recommendations are highly effective when they are based on collaborative signals like co-purchase statistics. In certain online marketplaces, however, e.g., on online auction sites, constantly new items are added to the catalog. In such cases, complementary item recommendations are often based on item side-information due to a lack of interaction data. In this work, we propose a novel approach that can leverage both item side-information and labeled complementary item pairs to generate effective complementary recommendations for cold items, i.e., for items for which no co-purchase statistics yet exist. Given that complementary items typically have to be of a different category than the seed item, we technically maintain a latent space for each item category. Simultaneously, we learn to project distributed item representations into these category spaces to determine suitable recommendations. The main learning process in our architecture utilizes labeled pairs of complementary items. In addition, we adopt ideas from Cycle Generative Adversarial Networks (CycleGAN) to leverage available item information even in case no labeled data exists for a given item and category. Experiments on three e-commerce datasets show that our method is highly effective.