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.

翻译：互补商品推荐是现代电商网站的普遍功能。此类推荐基于协同信号（如共同购买统计）时效果显著。然而，在特定在线市场（如在线拍卖网站）中，不断有新商品加入目录。此类情况下，由于缺乏交互数据，互补商品推荐常基于商品侧信息。本文提出一种新方法，可同时利用商品侧信息与标注的互补商品对，为冷启动商品（即尚无共同购买统计的商品）生成有效互补推荐。鉴于互补商品通常需与种子商品分属不同类别，我们技术性地为每个商品类别维护一个潜在空间，同时学习将分布式商品表征投影至这些类别空间以确定合适推荐。我们的架构中，主要学习过程利用标注的互补商品对。此外，我们借鉴循环生成对抗网络（CycleGAN）的思想，利用可用商品信息，即使针对特定商品或类别缺乏标注数据也能生效。在三个电商数据集上的实验表明，我们的方法效果显著。