Search Results for author: Xin Shen

Found 20 papers, 3 papers with code

E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service

no code implementations LREC 2022 Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He

There has been a growing interest in developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations.

Dialogue Management Management

Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning

no code implementations RANLP 2021 Xin Shen, Wai Lam

Our method forces the network to learn the necessary features for all the words in the input, which alleviates the shortcut learning problem.

Domain Generalization Paraphrase Identification

Divide and Ensemble: Progressively Learning for the Unknown

no code implementations9 Oct 2023 Hu Zhang, Xin Shen, Heming Du, Huiqiang Chen, Chen Liu, Hongwei Sheng, Qingzheng Xu, MD Wahiduzzaman Khan, Qingtao Yu, Tianqing Zhu, Scott Chapman, Zi Huang, Xin Yu

In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions.

G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer

no code implementations25 Jun 2023 Yuchen Zhuang, Xin Shen, Yan Zhao, Chaosheng Dong, Ming Wang, Jin Li, Chao Zhang

The detection of the underlying shopping intentions of users based on their historical interactions is a crucial aspect for e-commerce platforms, such as Amazon, to enhance the convenience and efficiency of their customers' shopping experiences.

Sequential Recommendation

Text Is All You Need: Learning Language Representations for Sequential Recommendation

1 code implementation23 May 2023 Jiacheng Li, Ming Wang, Jin Li, Jinmiao Fu, Xin Shen, Jingbo Shang, Julian McAuley

In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets.

Representation Learning Sentence +1

Learning Personalized Page Content Ranking Using Customer Representation

no code implementations9 May 2023 Xin Shen, Yan Zhao, Sujan Perera, Yujia Liu, Jinyun Yan, Mitchell Goodman

We propose a deep learning based bandit algorithm that incorporates historical shopping behavior, customer latent shopping goals, and the correlation between customers and content categories.

Learning to Personalize Recommendation based on Customers' Shopping Intents

no code implementations9 May 2023 Xin Shen, Jiaying Shi, Sungro Yoon, Jon Katzur, Hanbo Wang, Jim Chan, Jin Li

In this work, we introduce Amazon's new system that explicitly identifies and utilizes each customer's high level shopping intents for personalizing recommendations.

Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance

no code implementations9 May 2023 Xin Shen, Xiaonan Zhao, Rui Luo

We compared the model performances among our approach, baseline approach, and 3 alternative approaches to leverage semantic features.

Attribute Classification +2

FishRecGAN: An End to End GAN Based Network for Fisheye Rectification and Calibration

no code implementations9 May 2023 Xin Shen, Kyungdon Joo, Jean Oh

We propose an end-to-end deep learning approach to rectify fisheye images and simultaneously calibrate camera intrinsic and distortion parameters.

Data Efficient Training with Imbalanced Label Sample Distribution for Fashion Detection

no code implementations7 May 2023 Xin Shen, Praful Agrawal, Zhongwei Cheng

Multi-label classification models have a wide range of applications in E-commerce, including visual-based label predictions and language-based sentiment classifications.

Attribute Multi-Label Classification

MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding

no code implementations27 Nov 2022 Meihuizi Jia, Lei Shen, Xin Shen, Lejian Liao, Meng Chen, Xiaodong He, Zhendong Chen, Jiaqi Li

Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair.

named-entity-recognition Named Entity Recognition +4

Information fusion approach for biomass estimation in a plateau mountainous forest using a synergistic system comprising UAS-based digital camera and LiDAR

no code implementations14 Apr 2022 Rong Huang, Wei Yao, Zhong Xu, Lin Cao, Xin Shen

The objective of this study was to quantify the aboveground biomass (AGB) of a plateau mountainous forest reserve using a system that synergistically combines an unmanned aircraft system (UAS)-based digital aerial camera and LiDAR to leverage their complementary advantages.

Efficient Medical Image Segmentation Based on Knowledge Distillation

1 code implementation23 Aug 2021 Dian Qin, Jiajun Bu, Zhe Liu, Xin Shen, Sheng Zhou, Jingjun Gu, Zhijua Wang, Lei Wu, Huifen Dai

To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network.

Image Segmentation Knowledge Distillation +3

Two-stage Training for Learning from Label Proportions

no code implementations22 May 2021 Jiabin Liu, Bo wang, Xin Shen, Zhiquan Qi, Yingjie Tian

Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data.

Vocal Bursts Valence Prediction

Learning to Select Context in a Hierarchical and Global Perspective for Open-domain Dialogue Generation

no code implementations18 Feb 2021 Lei Shen, Haolan Zhan, Xin Shen, Yang Feng

Open-domain multi-turn conversations mainly have three features, which are hierarchical semantic structure, redundant information, and long-term dependency.

Decoder Dialogue Generation +1

OT-LLP: Optimal Transport for Learning from Label Proportions

no code implementations1 Jan 2021 Jiabin Liu, Hanyuan Hang, Bo wang, Xin Shen, Zhouchen Lin

Learning from label proportions (LLP), where the training data are arranged in form of groups with only label proportions provided instead of the exact labels, is an important weakly supervised learning paradigm in machine learning.

Weakly-supervised Learning

A Novel Large-scale Ordinal Regression Model

no code implementations19 Dec 2018 Yong Shi, Huadong Wang, Xin Shen, Lingfeng Niu

Ordinal regression (OR) is a special multiclass classification problem where an order relation exists among the labels.


Label Consistent Fisher Vectors for Supervised Feature Aggregation

1 code implementation 2014 22nd International Conference on Pattern Recognition 2014 Quan Wang, Xin Shen, Meng Wang, Kim L. Boyer

In this paper, we present a simple and efficient way to add supervised information into Fisher vectors, which has become a popular image representation method for image classification and retrieval purposes in recent years.

Classification General Classification +2

Cannot find the paper you are looking for? You can Submit a new open access paper.