Search Results for author: Bo Long

Found 41 papers, 14 papers with code

Interactive Latent Knowledge Selection for E-Commerce Product Copywriting Generation

no code implementations ECNLP (ACL) 2022 Zeming Wang, Yanyan Zou, Yuejian Fang, Hongshen Chen, Mian Ma, Zhuoye Ding, Bo Long

As the multi-modal e-commerce is thriving, high-quality advertising product copywriting has gain more attentions, which plays a crucial role in the e-commerce recommender, advertising and even search platforms. The advertising product copywriting is able to enhance the user experience by highlighting the product’s characteristics with textual descriptions and thus to improve the likelihood of user click and purchase.

Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search

no code implementations20 Mar 2023 Binbin Wang, Mingming Li, Zhixiong Zeng, Jingwei Zhuo, Songlin Wang, Sulong Xu, Bo Long, Weipeng Yan

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) methods are prevailing.


Automatic Scene-based Topic Channel Construction System for E-Commerce

no code implementations6 Oct 2022 Peng Lin, Yanyan Zou, Lingfei Wu, Mian Ma, Zhuoye Ding, Bo Long

To conduct scene marketing for e-commerce platforms, this work presents a novel product form, scene-based topic channel which typically consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words.


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

Context-Consistent Semantic Image Editing with Style-Preserved Modulation

1 code implementation13 Jul 2022 Wuyang Luo, Su Yang, Hong Wang, Bo Long, Weishan Zhang

Semantic image editing utilizes local semantic label maps to generate the desired content in the edited region.

Automatic Generation of Product-Image Sequence in E-commerce

1 code implementation26 Jun 2022 Xiaochuan Fan, Chi Zhang, Yong Yang, Yue Shang, Xueying Zhang, Zhen He, Yun Xiao, Bo Long, Lingfei Wu

For a platform with billions of products, it is extremely time-costly and labor-expensive to manually pick and organize qualified images.

Automatic Controllable Product Copywriting for E-Commerce

1 code implementation21 Jun 2022 Xiaojie Guo, Qingkai Zeng, Meng Jiang, Yun Xiao, Bo Long, Lingfei Wu

Automatic product description generation for e-commerce has witnessed significant advancement in the past decade.

Aspect Extraction Language Modelling +2

Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile

1 code implementation4 Jun 2022 Dong Chen, Lingfei Wu, Siliang Tang, Xiao Yun, Bo Long, Yueting Zhuang

Moreover, when handling the data with noisy labels, the meta-learner could be extremely sensitive to label noise on a corrupted dataset.

Few-Shot Learning

Meta Policy Learning for Cold-Start Conversational Recommendation

1 code implementation24 May 2022 Zhendong Chu, Hongning Wang, Yun Xiao, Bo Long, Lingfei Wu

We propose to learn a meta policy and adapt it to new users with only a few trials of conversational recommendations.

Meta Reinforcement Learning Recommendation Systems +2

Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce

no code implementations21 May 2022 Xueying Zhang, Kai Shen, Chi Zhang, Xiaochuan Fan, Yun Xiao, Zhen He, Bo Long, Lingfei Wu

In this paper, we proposed an automatic Scenario-based Multi-product Advertising Copywriting Generation system (SMPACG) for E-Commerce, which has been deployed on a leading Chinese e-commerce platform.

Language Modelling

Reducing Flipping Errors in Deep Neural Networks

1 code implementation16 Mar 2022 Xiang Deng, Yun Xiao, Bo Long, Zhongfei Zhang

Deep neural networks (DNNs) have been widely applied in various domains in artificial intelligence including computer vision and natural language processing.

Test unseen

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.


Feeding What You Need by Understanding What You Learned

no code implementations ACL 2022 Xiaoqiang Wang, Bang Liu, Fangli Xu, Bo Long, Siliang Tang, Lingfei Wu

In this paper, we argue that a deep understanding of model capabilities and data properties can help us feed a model with appropriate training data based on its learning status.

Machine Reading Comprehension

Sequential Search with Off-Policy Reinforcement Learning

no code implementations1 Feb 2022 Dadong Miao, Yanan Wang, Guoyu Tang, Lin Liu, Sulong Xu, Bo Long, Yun Xiao, Lingfei Wu, Yunjiang Jiang

Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time.

reinforcement-learning Reinforcement Learning (RL) +1

Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation

1 code implementation22 Dec 2021 Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Bo Long, Jian Pei

As a result, we first propose a more realistic CRS learning setting, namely Multi-Interest Multi-round Conversational Recommendation, where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances.


Automatic Product Copywriting for E-Commerce

no code implementations15 Dec 2021 Xueying Zhang, Yanyan Zou, Hainan Zhang, Jing Zhou, Shiliang Diao, Jiajia Chen, Zhuoye Ding, Zhen He, Xueqi He, Yun Xiao, Bo Long, Han Yu, Lingfei Wu

It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening.

Product Recommendation Text Generation

DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization

no code implementations SIGIR 2021 Xueying Zhang, Yunjiang Jiang, Yue Shang, Zhaomeng Cheng, Chi Zhang, Xiaochuan Fan, Yun Xiao, Bo Long

We propose a novel domain-specific generative pre-training (DS-GPT) method for text generation and apply it to the product titleand review summarization problems on E-commerce mobile display. First, we adopt a decoder-only transformer architecture, which fitswell for fine-tuning tasks by combining input and output all to-gether.

Text Generation

Learning to Generate Visual Questions with Noisy Supervision

1 code implementation NeurIPS 2021 Shen Kai, Lingfei Wu, Siliang Tang, Yueting Zhuang, Zhen He, Zhuoye Ding, Yun Xiao, Bo Long

The task of visual question generation (VQG) aims to generate human-like neural questions from an image and potentially other side information (e. g., answer type or the answer itself).

Question Generation Question-Generation +1

Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph

no code implementations20 Nov 2021 Hanning Gao, Lingfei Wu, Po Hu, Zhihua Wei, Fangli Xu, Bo Long

Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method.

Answer Selection Graph Question Answering +3

Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks

no code implementations20 Nov 2021 Hanning Gao, Lingfei Wu, Hongyun Zhang, Zhihua Wei, Po Hu, Fangli Xu, Bo Long

Most previous methods solve this task using a sequence-to-sequence model or using a graph-based model to encode RDF triples and to generate a text sequence.

reinforcement-learning Reinforcement Learning (RL) +1

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

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

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

1 code implementation8 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 Retrieval

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 Retrieval

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 +1

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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