SPM: Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search
Wen Zan, Yaopeng Han, Xiaotian Jiang, Yao Xiao, Yang Yang, Dayao Chen, Sheng Chen · 2023
In e-commerce search, relevance between query and documents is an essential requirement for satisfying user experience. Different from traditional e-commerce platforms that offer products, users search on life service platforms such as Meituan mainly for product providers, which usually have abundant structured information, e.g. name, address, category, thousands of products. Modeling search relevance with these rich structured contents is challenging due to the following issues: (1) there is language distribution discrepancy among different fields of structured document, making it difficult to directly adopt off-the-shelf pretrained language model based methods like BERT. (2) different fields usually have different importance and their length vary greatly, making it difficult to extract document information helpful for relevance matching.