CCGen: Explainable Complementary Concept Generation in E-Commerce

by   Jie Huang, et al.

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.


page 1

page 2

page 3

page 4


Explanations from Large Language Models Make Small Reasoners Better

Integrating free-text explanations to in-context learning of large langu...

ConceptDistil: Model-Agnostic Distillation of Concept Explanations

Concept-based explanations aims to fill the model interpretability gap f...

UCEpic: Unifying Aspect Planning and Lexical Constraints for Explainable Recommendation

Personalized natural language generation for explainable recommendations...

Concept Generation in Language Evolution

This thesis investigates the generation of new concepts from combination...

Complementary Explanations for Effective In-Context Learning

Large language models (LLMs) have exhibited remarkable capabilities in l...

Explaining Language Models' Predictions with High-Impact Concepts

The emergence of large-scale pretrained language models has posed unprec...

Succinct Representations for Concepts

Foundation models like chatGPT have demonstrated remarkable performance ...

Please sign up or login with your details

Forgot password? Click here to reset