Search Results for author: HengShu Zhu

Found 33 papers, 11 papers with code

Towards Faithful Neural Network Intrinsic Interpretation with Shapley Additive Self-Attribution

no code implementations27 Sep 2023 Ying Sun, HengShu Zhu, Hui Xiong

Self-interpreting neural networks have garnered significant interest in research.

Enhancing Job Recommendation through LLM-based Generative Adversarial Networks

no code implementations20 Jul 2023 Yingpeng Du, Di Luo, Rui Yan, Hongzhi Liu, Yang song, HengShu Zhu, Jie Zhang

However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion.

Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms

1 code implementation14 Jul 2023 Qi Liu, Zheng Gong, Zhenya Huang, Chuanren Liu, HengShu Zhu, Zhi Li, Enhong Chen, Hui Xiong

Machine learning algorithms have become ubiquitous in a number of applications (e. g. image classification).

Image Classification

Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations

no code implementations10 Jul 2023 Likang Wu, Zhaopeng Qiu, Zhi Zheng, HengShu Zhu, Enhong Chen

This paper focuses on unveiling the capability of large language models in understanding behavior graphs and leveraging this understanding to enhance recommendations in online recruitment, including the promotion of out-of-distribution (OOD) application.

Language Modelling Large Language Model +1

Generative Job Recommendations with Large Language Model

no code implementations5 Jul 2023 Zhi Zheng, Zhaopeng Qiu, Xiao Hu, Likang Wu, HengShu Zhu, Hui Xiong

The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process.

Collaborative Filtering Language Modelling +3

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

no code implementations3 Jul 2023 Chuan Qin, Le Zhang, Rui Zha, Dazhong Shen, Qi Zhang, Ying Sun, Chen Zhu, HengShu Zhu, Hui Xiong

To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management.

Decision Making Management

Reciprocal Sequential Recommendation

1 code implementation26 Jun 2023 Bowen Zheng, Yupeng Hou, Wayne Xin Zhao, Yang song, HengShu Zhu

Existing RRS models mainly capture static user preferences, which have neglected the evolving user tastes and the dynamic matching relation between the two parties.

Sequential Recommendation

A Survey on Large Language Models for Recommendation

1 code implementation31 May 2023 Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, HengShu Zhu, Qi Liu, Hui Xiong, Enhong Chen

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS).

Recommendation Systems Self-Supervised Learning

Preference or Intent? Double Disentangled Collaborative Filtering

no code implementations18 May 2023 Chao Wang, HengShu Zhu, Dazhong Shen, Wei Wu, Hui Xiong

In this way, the low-rating items will be treated as positive samples for modeling intents while the negative samples for modeling preferences.

Collaborative Filtering Disentanglement +1

Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph

1 code implementation18 May 2023 Chenguang Du, Kaichun Yao, HengShu Zhu, Deqing Wang, Fuzhen Zhuang, Hui Xiong

However, existing HGNNs usually represent each node as a single vector in the multi-layer graph convolution calculation, which makes the high-level graph convolution layer fail to distinguish information from different relations and different orders, resulting in the information loss in the message passing.

Information Retrieval Representation Learning +1

The Future of ChatGPT-enabled Labor Market: A Preliminary Study

no code implementations14 Apr 2023 Lan Chen, Xi Chen, Shiyu Wu, Yaqi Yang, Meng Chang, HengShu Zhu

To this end, in this paper, we introduce a preliminary data-driven study on the future of ChatGPT-enabled labor market from the view of Human-AI Symbiosis instead of Human-AI Confrontation.

Collaborative Filtering Language Modelling +1

A Survey on Causal Inference for Recommendation

no code implementations21 Mar 2023 Huishi Luo, Fuzhen Zhuang, Ruobing Xie, HengShu Zhu, Deqing Wang

Recently, causal inference has attracted increasing attention from researchers of recommender systems (RS), which analyzes the relationship between a cause and its effect and has a wide range of real-world applications in multiple fields.

Causal Inference Data Augmentation +1

A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection

no code implementations26 Nov 2022 Congxi Xiao, Jingbo Zhou, Jizhou Huang, HengShu Zhu, Tong Xu, Dejing Dou, Hui Xiong

The core idea of such a framework is to firstly pre-train a basis (or master) model over the URG, and then to adaptively derive specific (or slave) models from the basis model for different regions.


DOMFN: A Divergence-Orientated Multi-Modal Fusion Network for Resume Assessment

1 code implementation MM '22: Proceedings of the 30th ACM International Conference on Multimedia 2022 Yang Yang, Jingshuai Zhang, Fan Gao, Xiaoru Gao, HengShu Zhu

Inspired by practical resume evaluations that consider both the content and layout, we construct the multi-modalities from resumes but face a new challenge that sometimes the performance of multi-modal fusion is even worse than the best uni-modality.

Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification

no code implementations31 Dec 2021 Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, HengShu Zhu, Pengpeng Zhao, Chang Tan, Qing He

To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users.

Recommendation Systems

Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation

1 code implementation NeurIPS 2021 Ying Sun, HengShu Zhu, Chuan Qin, Fuzhen Zhuang, Qing He, Hui Xiong

To this end, in this paper, we aim to discern the decision-making processes of neural networks through a hierarchical voting strategy by developing an explainable deep learning model, namely Voting Transformation-based Explainable Neural Network (VOTEN).

Decision Making

Topic Modeling Revisited: A Document Graph-based Neural Network Perspective

1 code implementation NeurIPS 2021 Dazhong Shen, Chuan Qin, Chao Wang, Zheng Dong, HengShu Zhu, Hui Xiong

To this end, in this paper, we revisit the task of topic modeling by transforming each document into a directed graph with word dependency as edges between word nodes, and develop a novel approach, namely Graph Neural Topic Model (GNTM).

Variational Inference

Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness

1 code implementation24 Oct 2021 Dazhong Shen, Chuan Qin, Chao Wang, HengShu Zhu, Enhong Chen, Hui Xiong

As one of the most popular generative models, Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference.

Variational Inference

Exploiting Cross-Modal Prediction and Relation Consistency for Semi-Supervised Image Captioning

no code implementations22 Oct 2021 Yang Yang, Hongchen Wei, HengShu Zhu, dianhai yu, Hui Xiong, Jian Yang

In detail, considering that the heterogeneous gap between modalities always leads to the supervision difficulty of using the global embedding directly, CPRC turns to transform both the raw image and corresponding generated sentence into the shared semantic space, and measure the generated sentence from two aspects: 1) Prediction consistency.

Image Captioning Informativeness

MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

no code implementations12 Jul 2021 Weijia Zhang, Hao liu, Lijun Zha, HengShu Zhu, Ji Liu, Dejing Dou, Hui Xiong

Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e. g., real estate agents, appraisers, lenders, and buyers).

Decision Making Graph Representation Learning +1

Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks

no code implementations30 Dec 2020 Jindong Han, Hao liu, HengShu Zhu, Hui Xiong, Dejing Dou

Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations.

Graph Learning Multi-Task Learning

Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning

no code implementations16 Sep 2020 Denghui Zhang, Junming Liu, HengShu Zhu, Yanchi Liu, Lichen Wang, Pengyang Wang, Hui Xiong

However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for the same position (e. g., Programmer, Software Development Engineer, SDE, Implementation Engineer), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness modeling job transition patterns.

Benchmarking Link Prediction +1

Learning Adaptive Embedding Considering Incremental Class

1 code implementation31 Aug 2020 Yang Yang, Zhen-Qiang Sun, HengShu Zhu, Yanjie Fu, Hui Xiong, Jian Yang

To this end, we propose a Class-Incremental Learning without Forgetting (CILF) framework, which aims to learn adaptive embedding for processing novel class detection and model update in a unified framework.

class-incremental learning Class Incremental Learning +2

A Survey on Knowledge Graph-Based Recommender Systems

no code implementations28 Feb 2020 Qingyu Guo, Fuzhen Zhuang, Chuan Qin, HengShu Zhu, Xing Xie, Hui Xiong, Qing He

On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation.

Explainable Recommendation Recommendation Systems

SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback

1 code implementation23 Feb 2020 Chao Wang, HengShu Zhu, Chen Zhu, Chuan Qin, Hui Xiong

The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases.

Collaborative Ranking Recommendation Systems

A Machine Learning-enhanced Robust P-Phase Picker for Real-time Seismic Monitoring

no code implementations21 Nov 2019 Dazhong Shen, Qi Zhang, Tong Xu, HengShu Zhu, Wenjia Zhao, Zikai Yin, Peilun Zhou, Lihua Fang, Enhong Chen, Hui Xiong

To this end, in this paper, we present a machine learning-enhanced framework based on ensemble learning strategy, EL-Picker, for the automatic identification of seismic P-phase arrivals on continuous and massive waveforms.

BIG-bench Machine Learning Ensemble Learning

A Comprehensive Survey on Transfer Learning

3 code implementations7 Nov 2019 Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, HengShu Zhu, Hui Xiong, Qing He

In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments.

Transfer Learning

Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems

no code implementations1 Jun 2019 Binbin Jin, Enhong Chen, Hongke Zhao, Zhenya Huang, Qi Liu, HengShu Zhu, Shui Yu

Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (Q&A), where the multi-facet domain effects in CQA are still underexplored.

Answer Selection Community Question Answering

Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach

no code implementations21 Dec 2018 Chuan Qin, HengShu Zhu, Tong Xu, Chen Zhu, Liang Jiang, Enhong Chen, Hui Xiong

The wide spread use of online recruitment services has led to information explosion in the job market.

Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning

no code implementations8 Oct 2018 Chen Zhu, HengShu Zhu, Hui Xiong, Chao Ma, Fang Xie, Pengliang Ding, Pan Li

To this end, in this paper, we propose a novel end-to-end data-driven model based on Convolutional Neural Network (CNN), namely Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job.

Data Visualization Representation Learning

Recruitment Market Trend Analysis with Sequential Latent Variable Models

no code implementations8 Dec 2017 Chen Zhu, HengShu Zhu, Hui Xiong, Pengliang Ding, Fang Xie

To this end, in this paper, we propose a new research paradigm for recruitment market analysis by leveraging unsupervised learning techniques for automatically discovering recruitment market trends based on large-scale recruitment data.

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