Search Results for author: Wen-Yun Yang

Found 8 papers, 2 papers with code

Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search

1 code implementation12 Aug 2022 Yiming Qiu, Chenyu Zhao, Han Zhang, Jingwei Zhuo, TianHao Li, Xiaowei Zhang, Songlin Wang, Sulong Xu, Bo Long, Wen-Yun Yang

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: question answering, text classification, sequence labeling and so on.

Intent Detection Question Answering +3

Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes

no code implementations ICLR 2022 Yunjiang Jiang, Han Zhang, Yiming Qiu, Yun Xiao, Bo Long, Wen-Yun Yang

Product quantization (PQ) coupled with a space rotation, is widely used in modern approximate nearest neighbor (ANN) search systems to significantly compress the disk storage for embeddings and speed up the inner product computation.

Quantization

SearchGCN: Powering Embedding Retrieval by Graph Convolution Networks for E-Commerce Search

no code implementations1 Jul 2021 Xinlin Xia, Shang Wang, Han Zhang, Songlin Wang, Sulong Xu, Yun Xiao, Bo Long, Wen-Yun Yang

Graph convolution networks (GCN), which recently becomes new state-of-the-art method for graph node classification, recommendation and other applications, has not been successfully applied to industrial-scale search engine yet.

Node Classification Retrieval

Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index

1 code implementation9 May 2021 Han Zhang, Hongwei Shen, Yiming Qiu, Yunjiang Jiang, Songlin Wang, Sulong Xu, Yun Xiao, Bo Long, Wen-Yun Yang

Embedding index that enables fast approximate nearest neighbor(ANN) search, serves as an indispensable component for state-of-the-art deep retrieval systems.

Quantization Retrieval

A unified Neural Network Approach to E-CommerceRelevance Learning

no code implementations26 Apr 2021 Yunjiang Jiang, Yue Shang, Rui Li, Wen-Yun Yang, Guoyu Tang, Chaoyi Ma, Yun Xiao, Eric Zhao

We describe a highly-scalable feed-forward neural model to provide relevance score for (query, item) pairs, using only user query and item title as features, and both user click feedback as well as limited human ratings as labels.

Information Retrieval Retrieval

Fine-tune BERT for E-commerce Non-Default Search Ranking

no code implementations21 Aug 2020 Yunjiang Jiang, Yue Shang, Hongwei Shen, Wen-Yun Yang, Yun Xiao

The quality of non-default ranking on e-commerce platforms, such as based on ascending item price or descending historical sales volume, often suffers from acute relevance problems, since the irrelevant items are much easier to be exposed at the top of the ranking results.

Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning

no code implementations3 Jun 2020 Han Zhang, Songlin Wang, Kang Zhang, Zhiling Tang, Yunjiang Jiang, Yun Xiao, Weipeng Yan, Wen-Yun Yang

Two critical challenges stay in today's e-commerce search: how to retrieve items that are semantically relevant but not exact matching to query terms, and how to retrieve items that are more personalized to different users for the same search query.

Retrieval Semantic Retrieval

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