A Regularised Intent Model for Discovering Multiple Intents in E-Commerce Tail Queries

A substantial portion of the query volume for e-commerce search engines consists of infrequent queries and identifying user intent in such tail queries is critical in retrieving relevant products. The intent of a query is defined as a labelling of its tokens with the product attributes whose values are matched against the query tokens during retrieval. Tail queries in e-commerce search tend to have multiple correct attribute labels for their tokens due to multiple valid matches in the product catalog. In this paper, we propose a latent variable generative model along with a novel data dependent regularisation technique for identifying multiple intents in such queries. We demonstrate the superior performance of our proposed model against several strong baseline models on an editorially labelled data set as well as in a large scale online A/B experiment at Flipkart, a major Indian e-commerce company.