  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.
