Mapping a search query to a set of relevant categories in the product taxonomy is a significant challenge in e-commerce search for two reasons: 1) Training data exhibits severe class imbalance problem due to biased click behavior, and 2) queries with little customer feedback (e. g., tail queries) are not well-represented in the training set, and cause difficulties for query understanding.
Beyond our application, this TripleLearn framework, as well as the end-to-end process, is model-independent and problem-independent, so it can be generalized to more industrial applications, especially to the eCommerce industry which has similar data foundations and problems.
Entity-based semantic search has been widely adopted in modern search engines to improve search accuracy by understanding users' intent.
Retrieving all semantically relevant products from the product catalog is an important problem in E-commerce.
In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query.
A large-scale job title classification system can power various downstream applications such as semantic search, job recommendations and labor market analytics.
Entity Resolution, also called record linkage or deduplication, refers to the process of identifying and merging duplicate versions of the same entity into a unified representation.
Document classification for text, images and other applicable entities has long been a focus of research in academia and also finds application in many industrial settings.