We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e.g., "Digital Cameras", generating a list of complementary concepts, e.g., 1) Camera Lenses 2) Batteries 3) Camera Cases 4) Memory Cards 5) Battery Chargers. CCGen is beneficial for various applications like query suggestion and item recommendation, especially in the e-commerce domain. To solve CCGen, we propose to train language models to generate ranked lists of concepts with a two-step training strategy. We also teach the models to generate explanations by incorporating explanations distilled from large teacher models. Extensive experiments and analysis demonstrate that our model can generate high-quality concepts complementary to the input concept while producing explanations to justify the predictions.

翻译：我们提出并研究了互补概念生成（CCGen）任务：给定一个感兴趣的概念，例如“数码相机”，生成一组互补概念列表，例如1）相机镜头 2）电池 3）相机包 4）存储卡 5）电池充电器。CCGen对查询推荐和商品推荐等多种应用场景十分有益，尤其在电子商务领域。为解决CCGen问题，我们提出了一种采用两步训练策略的语言模型训练方法，用于生成排序后的概念列表。同时，通过融入从大型教师模型中蒸馏得到的解释信息，我们教会模型生成相应的解释。大量实验与分析表明，我们的模型能够生成与输入概念互补的高质量概念，同时生成解释以论证预测的合理性。