24 citations · 56 across the 7 of their papers we have counts for
7 papers
Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce
Juan Gong, Zhenlin Chen, Chaoyi Ma +7
Ranking model plays an essential role in e-commerce search and recommendation. An effective ranking model should give a personalized ranking list for each user according to the use…
Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search
Binbin Wang, Mingming Li, Zhixiong Zeng +5
Retrieving relevant items that match users' queries from billion-scale corpus forms the core of industrial e-commerce search systems, in which embedding-based retrieval (EBR) metho…
Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search
Yiming Qiu, Chenyu Zhao, Han Zhang +7
BERT-style models pre-trained on the general corpus (e.g., Wikipedia) and fine-tuned on specific task corpus, have recently emerged as breakthrough techniques in many NLP tasks: qu…
Deep Search Query Intent Understanding
Xiaowei Liu, Weiwei Guo, Huiji Gao +1
Understanding a user's query intent behind a search is critical for modern search engine success. Accurate query intent prediction allows the search engine to better serve the user…
Efficient Neural Query Auto Completion
Sida Wang, Weiwei Guo, Huiji Gao +1
Query Auto Completion (QAC), as the starting point of information retrieval tasks, is critical to user experience. Generally it has two steps: generating completed query candidates…
DeText: A Deep Text Ranking Framework with BERT
Weiwei Guo, Xiaowei Liu, Sida Wang +8
Ranking is the most important component in a search system. Mostsearch systems deal with large amounts of natural language data,hence an effective ranking system requires a deep un…