赵洪科老师入职天津大学管理与经济学部！

Cube成立

2018年11月

Cube获得第一项研究资助（百度开放研究课题）！

获得研究资助

2020年1月

Cube获得研究生招生资格！

招收研究生

2020年7月

Cube首批硕士研究生毕业！

首批毕业生

2022年12月

吴李康老师入职天津大学管理与经济学部！

吴李康老师加入

2024年6月

Cube's 100th paper has been accepted/published!

Cube第100篇学术论文

2024年7月

项志晨同学参加KDD2024并做会议报告

赵洪科老师、吴李康老师参加第一届认知智能与大数据技术研讨会

赵洪科老师、吴李康老师参加 SWAIB 2024 会议

赵洪科老师、吴李康老师参加联想与百度交流会

Dr. Likang Wu's lecture on LLM4Rec

Dinner between A&DM-Cube and Prof. Zhenya Huang’s Students

Symposium between A&DM-Cube and Prof. Zhenya Huang’s Team

Meeting among Prof. Hongke Zhao and Dr. Chuang Zhao


"Multi-View Empowered Structural Graph Wordification for Language Models" and "Automated Creation of Reusable and Diverse Toolsets for Enhancing LLM Reasoning" have been accepted by AAAI 2025.


Two papers are accepted at AAAI 2025


为鼓励和支持企业在人工智能学会平台开展基于企业科技发展需求、推动产学合作，共塑产学研合作的生态系统，为学术与产业的深度融合架起沟通的桥梁，联想集团依托学会在人工智能领域的影响力，共同设立“CAAI-联想蓝天科研基金项目“，面向高校和科研院所的学者开放研究问题。[相关链接](https://cie.caai.cn/index.php?s=/home/article/detail/id/4309.html)


Cube吴李康老师获批CAAI-联想蓝天科研基金资助！


Algorithm & Data Management Cube（算法与数据管理研究小组，A&DM-Cube）是隶属于天津大学管理与经济学部信息管理与管理科学系/复杂管理系统实验室的研究团队。为了进一步加强团队建设，推动科研工作的深入发展，现诚挚邀请优秀的本科生（不限本学部/校）加入（限5-10人，为保证培养的连续性，Cube除定期招募时间（本次截止至2024-07-05）外将不接受其他时间的同学的加入；参考培养细化方案，兴趣和投入难以保障者慎入！）。[点击下载文件](/file/ADM-Cube招募公告及培养方案.pdf)


A&DM-Cube招生简章


恭喜赵闯论文《Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment》被 TSMC 接收。TSMC 为中国科学院一区 top 期刊 IEEE Transactions on Systems, Man and Cybernetics: System。


恭喜赵闯论文《Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment》被TSMC接收。TSMC为中国科学院一区top期刊IEEE Transactions on Systems, Man and Cybernetics: System。

TSMC Accepted!


恭喜项志晨论文《Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems》被 TSMC 接收。KDD 全称为 Knowledge Discovery and Data Mining，即知识发现与数据挖掘，是由 ACM（Association for Computing Machinery，计算机协会）主办的国际性学术会议。该会议每年召开一次，旨在提供一个交流学术研究成果、展示最新技术、分享经验和思想的平台。KDD 是国际上著名的数据挖掘和知识发现领域的重要会议，被认为是该领域中的顶级会议之一。

恭喜项志晨论文《Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems》被 TSMC 接收。KDD 全称为 Knowledge Discovery and Data Mining，即知识发现与数据挖掘，是由 ACM（Association for Computing Machinery，计算机协会）主办的国际性学术会议。该会议每年召开一次，旨在提供一个交流学术研究成果、展示最新技术、分享经验和思想的平台。KDD 是国际上著名的数据挖掘和知识发现领域的重要会议，被认为是该领域中的顶级会议之一。


恭喜项志晨论文《Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems》被TSMC接收。KDD全称为Knowledge Discovery and Data Mining，即知识发现与数据挖掘，是由ACM（Association for Computing Machinery，计算机协会）主办的国际性学术会议。该会议每年召开一次，旨在提供一个交流学术研究成果、展示最新技术、分享经验和思想的平台。KDD是国际上著名的数据挖掘和知识发现领域的重要会议，被认为是该领域中的顶级会议之一。

KDD Accepted!


Hao Wei achieves his Master's Degree in 2024.


Congratulations on Hao Wei's graduation!


Welcome Doctor Wu joining the Data&Algorithm Management Lab in 2024!


Welcome Likang Wu joing the team!


Haipeng Yuan achieves his Master's Degree in 2023.


Congratulations on Haipeng Yuan's graduation!

Exploring the interpretability of vision GCN with a multi-method approach

GCTN: Graph Competitive Transfer Network for Cross-Domain Multi-Behavior Prediction

Multi-Objective Graph Contrastive Learning for Recommendation

An efficient continuous control perspective for reinforcement-learning-based sequential recommendation

Knowledge Enhanced Graph Contrastive Learning for Match Outcome Prediction

低碳神经网络算法：压缩和加速技术的发展与应用

Team formation in large organizations: A deep reinforcement learning approach

Promoting Machine Abilities of Discovering and Utilizing Knowledge in a Unified Zero-shot Learning Paradigm

Caption matters: a new perspective for knowledge-based visual question answering

A Semi-supervised Reject Inference Framework with Hierarchical Heterogeneous Networks for Credit Scoring

Enhancing Precision Drug Recommendations via In-depth Exploration of Motif Relationships

GTR: An Explainable Graph Topic-Aware Recommender for Scholarly Document

语言模型增强的引文网络连边因子挖掘

Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment

MOREM: An Evolutionary Multitasking Optimization Algorithm for Multi-Objective Recommendations

亲社会众筹平台上第三方认证对支持者的财务动机和利他动机的影响

Supporting Your Idea Reasonably: A Knowledge-Aware Topic Reasoning Strategy for Citation Recommendation

A Multi-Population Based Evolutionary Algorithm for Many-Objective Recommendations

Does academic engagement with industry come at a cost for early career scientists? Evidence from high-tech enterprises’ Ph.D. funding programs

Free trial or no free trial: optimal operation of electronic physical product with positive and negative network effects

An Ensemble Learning Approach with Gradient Resampling for Class-imbalance Problems

Cross-Domain Recommendation via Progressive Structural Alignment

Multi-View Enhanced Zero-Shot Node Classification

SHGCN: Socially Enhanced Heterogeneous Graph Convolutional Network for Multi-Behavior Prediction

A General Tail Item Representation Enhancement Framework for Sequential Recommendation

A Multi-aspect Neural Tensor Factorization Framework for Patent Litigation Prediction

What is Market Talking about? Market-oriented Prospect Analysis for Entrepreneur Fundraising

Learning the Explainable Semantic Relations via Unified Graph Topic-Disentangled Neural Networks

Learning Relation-Enhanced Hierarchical Solver for Math Word Problems

Forecasting movements of stock time series based on hidden state guided deep learning approach

A Community Division-Based Evolutionary Algorithm for Large-Scale Multi-Objective Recommendations

众筹项目筹资绩效的信息影响机制研究：基于Indiegogo平台证据

Lender Retention of Online Prosocial Lending: A Self-Determination Perspective

Personal or General? A Hybrid Strategy with Multi-factors for News Recommendation

A Sequence and Network Embedding Method for Bus Arrival Time Prediction using GPS Trajectory Data Only

Composing Like an Ancient Chinese Poet: Learn to Generate Rhythmic Chinese Poetry

Personalized Customer Lifetime Value Prediction in Online Games

CoEA: A Cooperative-Competitive Evolutionary Algorithm for Bidirectional Recommendations

Refined Analysis and a Hierarchical Multi-task Learning Approach for Loan Fraud Detection

The Effects of Person-Organization Fit on Lending Behaviors: Empirical Evidence from Kiva.org

Exploring the Effects of Social Capital on Crowdfunding Performance: A Holistic Analysis from the Empirical and Predictive Views

Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective

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

Toward Intelligent Financial Advisors for Identifying Potential Clients: A Multitask Perspective

Rethinking the Development of Technology Enhanced Learning and the Role of Cognitive Computing

Personalized Recommendation in P2P Lending Based on Risk-Return Management: A Multi-Objective Perspective

Dual-Stream Generative Adversarial Networks for Distributionally Robust Zero-Shot Learning

Can Cooperation Drive the Success of Suppliers in B2B Crowdsourcing Innovation Projects? A Large Scale Data Perspective

Voice of Charity: Prospecting the Donation Recurrence & Donor Retention in Crowdfunding

基于动静态表征的众筹协同预测方法

基于层级注意力机制的互联网用户信用评估框架

An effectiveness analysis of altmetrics indices for different levels of artificial intelligence publications

Product Supply Optimization for Crowdfunding Campaigns

A Structure-Enriched Neural Network for Network Embedding

基于跨域对抗学习的零样本分类

基于深度神经网络结构的互联网金融市场动态预测

实值优化问题的非对称负相关搜索算法

A Sequential Approach to Market State Modeling and Analysis in Online P2P Lending

Finding Potential Lenders in P2P Lending: A Hybrid Random Walk Approach

Illuminating Recommendation by Understanding the Explicit Item Relations

P2P Lending Survey: Platforms, Recent Advances and Prospects

用户在线购买预测:一种基于用户操作序列和选择模型的方法

网络小额贷款平台产品多维度分析与全局推荐

Mitigating Redundancy in Deep Recommender Systems: A Field Importance Distribution Perspective.

Instruct-of-Reflection: Enhancing Large Language Models Iterative Reflection Capabilities via Dynamic-Meta Instruction

Multi-View Empowered Structural Graph Wordification for Language Models

Automated Creation of Reusable and Diverse Toolsets for Enhancing LLM Reasoning

A survey on large language models for recommendation

Enhancing Code Search Intent with Programming Context Exploration

I-AM-G: Interest Augmented Multimodal Generator for Item Personalization

Rethinking Offline Reinforcement Learning for Sequential Recommendation from A Pair-Wise Q-Learning Perspective

FZR:Enhancing Knowledge Transfer via Shared Factors Composition in Zero-Shot Relational Learning

Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems

Cross-reconstructed Augmentation for Dual-target Cross-domain Recommendation

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

Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense

APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation

GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation

Knowledge-Aware Cross-Semantic Alignment for Domain-Level Zero-Shot Recommendation

Sequential recommendation via an adaptive cross-domain knowledge decomposition

Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation

KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification

Cross-domain recommendation via user interest alignment

Understanding and predicting innovative potential of scholars based on deep learning method

Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction

Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis

Winning Tracker: A New Model for Real-time Winning Prediction in MOBA Games

Multi Dimensional Prediction of Guild Health in Online Games: A Stability-Aware Multi-task Learning Approach

Co-Promotion Predictions of Financing Market and Sales Market: A Cooperative-Competitive Attention Approach

Outside In: Market-aware Heterogeneous Graph Neural Network for Employee Turnover Prediction

TGNN: A GNN-Based Method with Multi-entity Node for Personal Banking Time Prediction

LawyerPAN: A Proficiency Assessment Network for Trial Lawyers

Preference-Adaptive Meta-Learning for Cold-Start Recommendation

Coupling Macro-Sector-Micro Factors on Learning Stock Representation with Less Uncertainty

HMS: A Hierarchical Solver with Dependency-Enhanced Understanding for Math Word Problem

Learning the Implicit Semantic Representation on Graph-Structured Data

Enhanced Representation Learning for Examination Papers with Hierarchical Document Structure

A Machine Learning Method for Measuring Information Disclosure in Sharing Economy Platforms

Learning the Compositional Visual Coherence for Complementary Recommendations

Adaptive Quantitative Trading: an Imitative Deep Reinforcement Learning Approach

跨域推荐和交互行为序列处理方法及相关设备

一种基于大语言模型的在线招聘推荐系统及方法

基于视觉组合关系学习的时尚服装智能搭配与推荐方法

一种基于多交互式注意力网络的点击率预测方法与装置

基于图学习和不确定行为建模的胜率预测系统及方法

跨域对抗神经网络模型及其训练方法,设备及存储介质

用于处理技能信息的方法、模型训练方法及装置

一种基于区块链技术的农业众筹平台

一种基于农村强社交关系的智能社交平台及社交方法

训练预测模型的方法、预测方法、装置、电子设备和介质

一种问答匹配注意力处理方法、计算机设备及存储介质

一种专利诉讼风险预测方法

基于动静态深度表征的协同预测方法

网络小额贷款中借款项目与出借人的双向匹配推荐方法

一种社区问答平台回答排序方法

一种基于用户行为区别建模的序列推荐方法

丰富结构信息的深度网络表征方法

一种跨领域情感分类方法和相关装置

一种中文文本的分类方法及系统

雄安新区综合数据平台(二期)项目建设咨询

IEEE Standard P3154 (Recommended Practice for the Application of Knowledge Graph for Talent Services)