Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce

Product searching is fundamental in online e-commerce systems, it needs to quickly and accurately find the products that users required. Relevance is essential for e-commerce search, which role is avoiding displaying products that do not match search intent and optimizing user experience. Measuring semantic relevance is necessary because distributional biases between search queries and product titles may lead to large lexical differences between relevant textual expressions. Several problems limit the performance of semantic relevance learning, including extremely long-tail product distribution and low-quality labeled data. Recent works attempt to conduct relevance learning through user behaviors. However, noisy user behavior can easily cause inadequately semantic modeling. Therefore, it is valuable but challenging to utilize user behavior in relevance learning. In this paper, we first propose a weakly supervised contrastive learning framework that focuses on how to provide effective semantic supervision and generate reasonable representation. We utilize topology structure information contained in a user behavior heterogeneous graph to design a semantically aware data construction strategy. Besides, we propose a contrastive learning framework suitable for e-commerce scenarios with targeted improvements in data augmentation and training objectives. For relevance calculation, we propose a novel hybrid method that combines fine-tuning and transfer learning. It eliminates the negative impacts caused by distributional bias and guarantees semantic matching capabilities. Extensive experiments and analyses show the promising performance of proposed methods in relevance learning.