Search Results for author: Bo Long

Found 17 papers, 3 papers with code

Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation

no code implementations24 Sep 2021 Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long

In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation.

Graph Learning

Multi-behavior Graph Contextual Aware Network for Session-based Recommendation

no code implementations24 Sep 2021 Qi Shen, Lingfei Wu, Yitong Pang, Yiming Zhang, Zhihua Wei, Fangli Xu, Bo Long

Based on the global graph, MGCNet attaches the global interest representation to final item representation based on local contextual intention to address the limitation (iii).

Session-Based Recommendations

Deep Natural Language Processing for LinkedIn Search

no code implementations16 Aug 2021 Weiwei Guo, Xiaowei Liu, Sida Wang, Michaeel Kazi, Zhiwei Wang, Zhoutong Fu, Jun Jia, Liang Zhang, Huiji Gao, Bo Long

Building a successful search system requires a thorough understanding of textual data semantics, where deep learning based natural language processing techniques (deep NLP) can be of great help.

Document Ranking Language Modelling

Deep Natural Language Processing for LinkedIn Search Systems

no code implementations30 Jul 2021 Weiwei Guo, Xiaowei Liu, Sida Wang, Michaeel Kazi, Zhoutong Fu, Huiji Gao, Jun Jia, Liang Zhang, Bo Long

Many search systems work with large amounts of natural language data, e. g., search queries, user profiles and documents, where deep learning based natural language processing techniques (deep NLP) can be of great help.

Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

no code implementations8 Jul 2021 Yitong Pang, Lingfei Wu, Qi Shen, Yiming Zhang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long, Jian Pei

Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions.

Session-Based Recommendations

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

Improving Sequential Recommendation Consistency with Self-Supervised Imitation

no code implementations26 Jun 2021 Xu Yuan, Hongshen Chen, Yonghao Song, Xiaofang Zhao, Zhuoye Ding, Zhen He, Bo Long

In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation.

Imitation Learning Sparse Learning

Graph Neural Networks for Natural Language Processing: A Survey

1 code implementation10 Jun 2021 Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP).

graph construction Graph Representation Learning

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

Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search

no code implementations13 Jan 2021 Ziyang Liu, Zhaomeng Cheng, Yunjiang Jiang, Yue Shang, Wei Xiong, Sulong Xu, Bo Long, Di Jin

We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction.

Network Embedding

Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability

no code implementations16 Sep 2020 Ninghao Liu, Yunsong Meng, Xia Hu, Tie Wang, Bo Long

Recent years have witnessed an increasing number of interpretation methods being developed for improving transparency of NLP models.

Deep Search Query Intent Understanding

no code implementations15 Aug 2020 Xiao-Wei Liu, Weiwei Guo, Huiji Gao, Bo Long

Understanding a user's query intent behind a search is critical for modern search engine success.

Efficient Neural Query Auto Completion

no code implementations6 Aug 2020 Sida Wang, Weiwei Guo, Huiji Gao, Bo Long

On the candidate generation side, this system uses as much information as possible in unseen prefixes to generate relevant candidates, increasing the recall by a large margin.

Information Retrieval Language Modelling

Memory-efficient Embedding for Recommendations

no code implementations26 Jun 2020 Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, Bo Long

Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.

AutoML Recommendation Systems

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