郭贵冰，博士，长聘教授，辽宁省百千万人才

    邮  箱：guogb@swc.neu.edu.cn
    电  话：+86-024-83683332
    主  页：https://guoguibing.github.io
    地  址：辽宁省沈阳市浑南区创新路195号东北大学浑南校区信息楼B436室, 110169

常用链接

    国家自然科学基金查询
    SCI 中科院分区查询
    CCF 会议期刊排名
    教师列表、校历、课表

个人简介

辽宁省百千万人才，东北大学软件学院引进人才。师从新加坡南洋理工大学(NTU)的Assoc. Prof. Jie Zhang和Prof. Daniel Thalmann，2015年7月获得博士学位；2015年1~10月，在新加坡管理大学(SMU)朱飞达教授的实验室担任研究员。2015年11月，聘为东北大学软件学院副教授。2019年7月破格评为教授，同年12月评为东北大学长聘教授、辽宁省百千万人才。

LibRec推荐系统库创始人，SCI二区期刊《Electronic Commerce Research and Applications》编委，中国计算机学会会员，中国人工智能学会智能服务专业委员会委员。作为大会主席策划举办了EdRecSys 2016、IFUP 2016、IFUP 2018 国际研讨会；受邀成为多个重要国际学术会议的程序委员会委员，包括AAAI、IJCAI、SIGIR、WI、RecSys等；受邀成为多个重要国际学术会议和期刊的审稿人，包括AAAI、WWW、SIGIR、RecSys、TKDE、KAIS、KBS、ECRA、WIAS等。

研究兴趣包括 推荐系统、智能问答、自然语言处理、数据挖掘。在相关研究领域已发表60余篇国际学术会议和期刊文章，Google学术H-index：19，总引用：1700 (Google学术页面)。出版了一部学术专著：《推荐系统进展：方法与技术》（点此购买）。目前的主要研究内容包括以下两个方面：

    推荐系统：重点是面向大数据的深度协同推荐算法，融合文本、图像、社交、知识图谱等多模态信息、以深度学习技术为基础的新型推荐系统。推荐系统是现代Web应用的核心技术之一，通过对大数据的用户偏好行为进行分析和建模，学习出目标用户的偏好模式，从而给出精准的个性化物品推荐。常见的推荐方式包括“买过...的还买过...”，“看过...的还看过...”等，涵盖的领域包括电影、音乐、新闻、商品、旅游、教育等，应用范围非常广泛，是大数据时代最典型的应用之一。
    智能问答：重点是基于知识图谱的智能问答算法，以深度学习技术为基础，探索融合知识图谱信息的精准问答匹配技术。其挑战主要包括：如何理解并有效表示问题；如何将问题映射为结构化查询；如何生成自然语言答案等。常见的智能问答系统如客服和聊天机器人、Siri等，可以分为基于事实的问答系统、基于常见问题集的问答系统和开放域的问答系统等三类。相关的研究课题还包括机器阅读理解、序列模型和语言模型等内容。

学术科研与应用
研究课题

基本概况

课题组现有本科生、硕士研究生和外国留学生等三十余人，计划每年招收6~8名硕士研究生、6~8名本科生。课题组与新加坡南洋理工大学(NTU)、新加坡管理大学(SMU)有密切联系，表现突出的同学有机会被推荐到新加坡交换学习，进一步深造。

    研究生培养模式：合作导师制。
        为每个有潜力的研究生，邀请一位经验丰富的学者作为合作导师。
        为每个有潜力的研究生，搭配一位高年级的本科生进行结伴研究。
        学生、导师、合作导师就共同感兴趣的研究课题进行深度合作。
        合作导师：国内外著名高校博士、博士后、教授及企业老师等。
        学生要求：
            为人真诚、勤学好问；英语（CET-6）、数学、编程能力强；
            学习过深度学习课程，使用过TensorFlow或PyTorch的学生优先；
        获得国家奖学金的研究生：翟松林、欧阳世昌、孟媛、杨恩能
    科研论文列表，Google学术论文引用
    课题组资源汇总

主持的课题项目

    2020.1.1 - 2023.12.31，国家自然科学基金面上项目，基于多类型用户偏好和物品属性增强的序列推荐技术研究，61972078，58万元。
    2018.1.1 - 2020.12.31，国家自然科学基金青年科学基金项目，基于多维辅助反馈的数据生成和物品推荐机理研究，61702084，25万元。
    2019.1.1 - 2021.12.31，教育部项目基本科研业务费项目优秀青年科技人才培育项目，基于多态行为轨迹的个性化推荐技术研究，N181705007，25万元。
    2018.9.1 - 2019.5.31，京东AI研究院“京东葡萄树--学者计划”，基于知识图谱和图嵌入技术的推荐技术研究。
    2017.9.1 - 2018.8.31，华为HIRP OPEN 2017基金项目，基于深度学习和生成式对抗神经网络技术的推荐系统模型，20万元。
    2017.5.1 - 2019.4.30，辽宁省自然科学基金指导计划面上项目，20170540319，5万元。
    2017.1.1 - 2018.12.31，教育部基本科研业务费国家项目培育种子基金，N161704001，22万元。
    2016.6 - 2021.5，东北大学软件学院“青苹果”计划 - 优秀青年骨干教师培养计划，15万元。

“数据科学” 科研团队

数据科学 科研团队目前由三位教师构成，分别是：郭贵冰（团队负责人）、刘园、姜琳颖。团队的主要研究方向包括：推荐系统、区块链技术、自然语言处理。团队拟每年招收12名硕士研究生、数十名高年级本科生参与三大研究方向的学术研究。
推荐系统：

    基于会话序列的推荐模型
    基于知识图谱的推荐模型
    基于强化学习的推荐模型
    推荐模型的攻击与防御
    推荐模型的可解释性、新颖性
    关联项目：LibRec、LibDL

自然语言处理：

    基于知识图谱的问答系统
    机器阅读理解
    自动文本摘要
    信息检索
    关联项目：IdeaMan、LibDL

区块链技术：

    区块链公平高效共识机制研究
    区块链在物联网的应用
    区块链在车联网的应用
    具有隐私保护的信誉系统研究
    关联项目：DevChain


开源项目：LibRec，A Leading Java Library for Recommender Systems

基本概况

是领先的推荐系统开源算法库，实现了90+个推荐算法，可解决评分预测和物品推荐两大关键问题。该开源库在GitHub的推荐系统领域排名第一，获得了大家的广泛认可和支持。当前的发展方向是支持大数据的工业应用。2016年1月，LibRec核心开发团队正式成立。到目前为止，共有20位成员参与开发，包括7名有多年研发经验的IT公司研发经理和职员，3名海外博士，5名国内博士，3名硕士研究生【查看团队成员详情】

核心团队

    Core 模块增强：负责推荐算法的代码实现、并行学习、增量更新，以及融合多个推荐算法的集成模型。
    Spark 模块开发：负责LibRec-Spark的通信桥接、基于Spark的高级推荐算法实现、工业级的扩展应用等。
    LibDL 框架开发：提出基于Java的自有深度学习框架LibDL、实现基于LibDL的深度推荐算法及功能扩展等。
    推广宣传：负责微信、微博、知乎等公众平台的帐号运营、各论坛的推广宣传等。

加入我们

    投递个人简历到 guogb@swc.neu.edu.cn
    通知面试
    加入团队

项目开发与研究
Python 项目：IdeaMan，“谋士”，A General Framework for Idea Management

项目简介

本项目旨在构建学术领域的通用管理框架，研究的主要内容有：（1）（由关键词定义的）特定研究领域的进展跟踪和内容推荐，如社交关注和论文发表；（2）学术作者的论文检索和影响计算，如作者关系图谱、领域影响力、文献管理等；（3）学术事件的及时跟进和个性推荐，如国际会议的举办、学术报告的推荐等。

团队组成

团队成立于2018年9月，目前由 5 名本科学生组成，欢迎更多学生参与。

项目进展

主要编程语言是Python，已经完成或正在进行的内容有：

    通过在Tweet、Arxiv、Reddit等多源信息渠道上抓取数据，完成了数据的相关性过滤、内容的领域分析、文档的简略摘要等，研究成果应用于【LibRec智能推荐】微信公众号的【LibRec精选】档目。
    PDF格式的论文内容分析：论文的作者、院校、邮箱信息的抓取、论文缩略图的生成、论文关键词（短语）的抽取等；
    学术作者的影响力分析：作者关系图的构建和展示、多ID作者的识别等。
    基于论文摘要、参考文献引用图、用户行为等要素的论文推荐；

项目展示
IdeaMan
Java 项目：LibDL，A Java Library for Deep Learning

项目简介

本项目旨在构建一个通用的基于Java语言的深度学习模架。深度学习已经成为业界热门的主题词，其业务应用已经从语音识别、计算机视觉扩展到自然语言处理等诸多领域。同时，也涌现了不少颇具影响力的深度学习框架，如Tensorflow、PyTorch等。但是，基于Java语言的深度学习框架相对较少，而业界也有相当的业务需求。

团队组成

团队成立于2018年11月，目前 5 名本科学生组成，欢迎更多学生参与。

项目进展

主要编程语言是Java，已经完成或正在进行的内容有：

    LibDL框架的顶层设计、功能模块划分、动态计算图等的讨论和分析；
    已完成文本、图像等源数据的读取、转换等操作，包括word2vec词嵌入等；
    已完成卷积神经网络CNN，包括卷积和池化操作，正在实现conv2d等函数。
    已完成循环神经网络RNN及双向RNN，正在处理LSTM、GRU等模型的实现。
    已完成除噪自编码器DAE的开发和测试，正在处理堆叠SDAE模型的实现。
    神经网络模型的序列化、反序列化等，模型训练学习的可视化等技术的实现。

项目展示
LibDL
教学课程与内容
课程：推荐系统导论

课程简介

推荐系统通过分析用户对一系列事物的历史偏好来发现其行为模式，然后从一些尚未经历过的物品中推荐一些感兴趣的物品。课程的主要内容包括：（1）基于内容的过滤算法；（2）基于用户反馈的协同过滤算法；（3）基于数据降维的隐式因子模型；（4）推荐系统的分析和评估；（5）推荐系统开源库 LibRec 及其应用。

课程内容

    Chapter 1: Introduction
    Chapter 2: Content-based Recommendation
    Chapter 3: Collaborative Filtering with LibRec
    Chapter 4: Matrix Factorization
    Chapter 5: Factorization Machine
    Chapter 6: Neural Recommendation
    Chapter 7: Sequential Recommendation
    Chapter 8: Industry Implementations

课程：自然语言处理

课程简介

本课程介绍自然语言处理的主要研究内容及关键技术，以及相关的研究成果。课程的主要内容包括：（1）数学基础，如贝叶斯模型、n元语法模型和 HMM模型等；（2）语言学基础，介绍有关词法和语法等方面的知识，包括词语搭配、语义消歧、句法分析等；（3）具体应用，如文本分类和聚类、信息检索、机器翻译等。

课程内容

    Chapter 1: Introduction
    Chapter 2: Basic Text Processing
    Chapter 3: N-gram Language Modeling
    Chapter 4: Text Classification
    Chapter 5: Part-of-Speech Tagging
    Chapter 6: Sequence Processing with Recurrent Networks
    Chapter 7: Question Answering and Summarization
    Chapter 8: Information Extraction

Journals

    VSE-fs: Fast Full-Sample Visual Semantic Embedding[PDF]
    S. Zhai, G. Guo*, F. Yuan, Y. Liu, X. Wang
    IEEE Intelligent Systems (IF: 4.464), accepted, 2020.
    Exploiting Review Embedding and User Attention for Item Recommendation[PDF]
    Y. Sun, G. Guo*, X. Chen, P. Zhang, X. Wang
    Knowledge and Information Systems (IF: 2.397), accepted, 2020.
    Multi-facet User Preference Learning for Fine-grained Item Recommendation[PDF]
    X. Zhou, G. Guo*, Z. Sun, Y. Liu
    Neurocomputing (IF: 4.072), accepted, 2020.
    Fast Discrete Factorization Machine for Personalized Item Recommendation[PDF]
    S. Qu, G. Guo*, Y. Liu, Y. Yao, W. Wei
    Knowledge-based Systems (IF: 5.101), accepted, 2020.
    Multi-level Coupling Network for Non-IID Sequential Recommendation[PDF]
    Y. Sun, G. Guo*, X. He, X. Liu
    IEEE Access (IF: 4.098), accepted, 2020.
    Visual Semantic Image Recommendation[PDF]
    G. Guo, Y. Meng, Y. Zhang, C. Han, Y. Li
    IEEE Access (IF: 3.557), 7, 33424 - 33433, Feb 2019.
    PCCF: Periodic and Continual Temporal Co-Factorization for Recommender Systems[PDF]
    G. Guo, F. Zhu, S. Qu, X. Wang
    Information Sciences (IS, IF: 4.832), 436-437, 56-73, Apr 2018.
    Search Engine based Proper Privacy Protection Scheme[PDF]
    G. Guo, T. Yang, Y. Liu
    IEEE Access (IF: 3.557), 6, 78551-78558, Dec 5, 2018.
    BPRH: Bayesian Personalized Ranking for Heterogeneous Implicit Feedback[PDF]
    H. Qiu, Y. Liu*, G. Guo, Z. Sun, J. Zhang, H. T. Nguyen
    Information Sciences (IS, IF: 4.832), 453, 80-98, Apr 2018.
    Factored Similarity Models with Social Trust for Top-N Item Recommendation[PDF | Code]
    G. Guo, J. Zhang, F. Zhu, X. Wang
    Knowledge-based Systems (KBS, IF: 4.529), 122, 17-25, Apr 15, 2017.
    Resolving Data Sparsity by Multi-type Auxiliary Implicit Feedback for Recommender Systems[PDF]
    G. Guo, H. Qiu, Z. Tan, Y. Liu, J. Ma, X. Wang
    Knowledge-based Systems (KBS, IF: 4.529), 138, 202-207, Dec 15, 2017.
    Leveraging Multi-actions to Improve Medical Personalized Ranking for Collaborative Filtering[PDF]
    S. Gao, G. Guo*, R. Li, Z. Wang
    Journal of Healthcare Engineering (IF: 0.965), Oct 3, 2017.
    Measuring Similarity of Users with Qualitative Preferences for Service Selection
    H. Wang, H. Wang, G. Guo, Y. Tang, J. Zhang
    Knowledge and Information Systems (KIS, IF: 2.004), 51(2), 561-594, May 2017.
    A Novel Recommendation Model Regularized with User Trust and Item Ratings[PDF | Code]
    G. Guo, J. Zhang, N. Yorke-Smith
    IEEE Transactions on Knowledge and Data Engineering (TKDE, IF: 3.438), 28(7), 1607-1620, 2016.
    The source codes of TrustSVD++ can be downloaded here, which is based on a previous version (v1.3) of LibRec.
    You may refer to the implementation of TrustSVD in LibRec for adapting and using TrustSVD++ in the new version.
    A Novel Evidence-based Bayesian Similarity Measure for Recommender Systems
    G. Guo, J. Zhang, N. Yorke-Smith
    ACM Transactions on the Web (TWEB, IF: 1.526), 10(2), 8:1-8:30, 2016.
    Leveraging Multiviews of Trust and Similarity to Enhance Cluster-based Recommender Systems[PDF]
    G. Guo, J. Zhang, N. Yorke-Smith
    Knowledge-Based Systems (KBS, IF: 3.325), 74(0), pp. 14-27, 2015.
    Multi-Faceted Trust and Distrust Prediction for Recommender Systems[PDF]
    H. Fang, G. Guo*, J. Zhang
    Decision Support Systems (DSS, IF: 2.604), 71(0), pp. 37-47, 2015.
    Integrating Trust with User Preference for Effective Web Service Composition
    H. Wang, B. Zou, G. Guo, J. Zhang, D. Yang
    IEEE Transactions on Services Computing (TSC, IF: 2.365), accepted, 2015.
    Merging Trust in Collaborative Filtering to Alleviate Data Sparsity and Cold Start[PDF]
    G. Guo, J. Zhang, D. Thalmann
    Knowledge-Based Systems (KBS, IF: 2.947), 57(0), pp. 57-68, 2014.
    Leveraging Prior Ratings for Recommender Systems in E-Commerce[PDF]
    G. Guo, J. Zhang, D. Thalmann, N. Yorke-Smith
    Electronic Commerce Research and Applications (ECRA, IF: 1.482), 13(6), pp. 440-455, 2014.
    Opinions of People: Factoring in Privacy and Trust[PDF]
    A. Basu, J. Vaidya, J.C. Corena, S. Kiyomoto, S. Marsh, G. Guo, J. Zhang, Y. Miyake
    ACM SIGAPP Applied Computing Review, 14(3), pp. 7-21, 2014.

Conferences

    Leveraging Title-Abstract Attentive Semantics for Paper Recommendation
    G. Guo, B. Chen, X. Zhang, Z. Liu, Z. Dong, X. He
    Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), 2020. (20.6% acceptance, 1591/7737)
    BiGAN: Collaborative Filtering with Bidirectional Generative Adversarial Networks
    R. Ding, G. Guo*, B. Chen, X. Yang
    Proceedings of the 20th SIAM International Conference on Data Mining (SDM), 2020. (24% acceptance, 75/312)
    Future Data Helps Training: Modelling Future Contexts for Session-based Recommendation
    F. Yuan, G. Guo*, C. He, etc.
    Proceedings of the 29th International Conference on the Web (WWW), 2020. (19% acceptance, 219/1129)
    Modelling Temporal Dynamics and Repeated Behaviors for Recommendation
    X. Zhou, Z. Sun, G. Guo*, etc.
    Proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2020. (21% acceptance, 131/628)
    Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation
    G. Guo, S. Ouyang, X. He, F. Yuan, X. Liu
    Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019. (17.9% acceptance, 850/4752)
    Discrete Trust-aware Matrix Factorization for Fast Recommendation
    G. Guo, E. Yang, L. Shen, X. Yang, X. He
    Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019. (17.9% acceptance, 850/4752)
    Multi-Hop Path Queries over Knowledge Graphs with Neural Memory Networks
    Q. Wang, H. Yin, W. Wang, Z. Huang, G. Guo, Q.V.H. Nguyen
    Proceedings of the 24th International Conference on Database Systems for Advanced Applications (DASFAA), 2019.
    Approximating Word Ranking and Negative Sampling for Word Embedding
    G. Guo, S. Ouyang, F. Yuan, X. Wang
    Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018. (20% acceptance, 710/3470)
    VSE-ens: Visual-Semantic Embeddings with Efficient Negative Sampling
    G. Guo, S. Zhai, F. Yuan, Y. Liu, X. Wang
    Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018.
    fBGD: Learning Embeddings from Positive-Only Data Without Sampling
    F. Yuan,X. Xin, X. He, G. Guo, W.Zhang, T. Chua, J. Jose
    Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), 2018.
    Team Expansion in Collaborative Environment
    L. Zhao, Y. Yao, G. Guo, H. Tong, F. Xu, J. Lu
    Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 713-725, 2018.
    Multi-view Visual Bayesian Personalized Ranking from Implicit Feedback
    H. Luo, X. Zhang, B. Chen, G. Guo
    Proceedings of the ACM Conference on User Modeling, Adaptation, and Personalization (UMAP), 2018. (extended abstract)
    LFSF: Latent Factor-Based Similarity Framework and Its Application for Collaborative Recommendation
    L. He, Z. Tan, G. Guo, Q. Chang, D. Wu
    Proceedings of the 19th Pacific-Rim Conference on Multimedia (PCM), pp. 744-754, 2018.
    BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation
    F. Yuan, G. Guo, J. Jose, L. Chen, H. Yu, W. Zhang
    Proceedings of the 22nd Annual Meeting of the Intelligent User Interfaces Community and Serves (IUI), 2017.
    Learning Hierarchical Category Influence on both Users and Items for Effective Recommendation
    Z. Sun, G. Guo, J. Zhang
    Proceedings of the 32nd ACM Symposium On Applied Computing (SAC), 2017.
    A Unified Latent Factor Model for Effective Category-Aware Recommendation
    Z. Sun, G. Guo, J. Zhang, C. Xu
    Proceedings of the 25th International Conference on User Modeling, Adaptation and Personalization (UMAP), 2017. (extended abstract)
    An Identity Management System Based on Blockchain
    Y. Liu, Z. Zhao, G. Guo, X. Wang, Z. Tan, S. Wang
    Proceedings of the 15th International Conference on Privacy, Security and Trust (PST), 2017
    A Reputation Model for Aggregating Ratings based on Beta Distribution Function
    Y. Liu, U. S. Chitawa, G. Guo, X. Wang, Z. Tan, S. Wang
    Proceedings of the 2nd International Conference on Crowd Science and Engineering (ICCSE), pp. 77-81, 2017
    LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates
    F. Yuan, G. Guo, J. Jose, W. Zhang, L. Chen, H. Yu
    Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM, 165/935=17.65%), pp. 227-236, 2016.
    Optimizing Factorization Machines for Top-N Context-Aware Recommendations
    F. Yuan, G. Guo, J. Jose, W. Zhang, L. Chen, H. Yu
    Proceedings of the 17th International Conference on Web Information Systems Engineering (WISE), pp. 278-293, 2016.
    Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation
    F. Yuan, J. Jose, G. Guo, L. Chen, H. Yu, R. Alkhawaldeh
    Proceedings of the 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016. (best student paper)
    Effective Recommendation with Category Hierarchy
    Z. Sun, G. Guo, J. Zhang
    Proceedings of the 24th International Conference on User Modeling, Adaptation and Personalization (UMAP), 2016. (extended abstract)
    TBPR: Trinity Preference based Bayesian Personalized Ranking for Multivariate Implicit Feedback
    H. Qiu, G. Guo, J. Zhang, H. Nguyen, Z. Sun, Y. Liu
    Proceedings of the 24th International Conference on User Modeling, Adaptation and Personalization (UMAP), 2016. (extended abstract)
    TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings[PDF, Code]
    G. Guo, J. Zhang, N. Yorke-Smith
    Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI, acceptance rate: 26.67%), 2015.
    LibRec: A Java Library for Recommender Systems[PDF, Code]
    G. Guo, J. Zhang, Z. Sun, N. Yorke-Smith
    Proceedings of the 23rd International Conference on User Modeling, Adaptation and Personalization (UMAP), 2015. (demo paper)
    Exploiting Implicit Item Relationships for Recommender Systems[PDF]
    Z. Sun, G. Guo, J. Zhang
    Proceedings of the 23rd International Conference on User Modeling, Adaptation and Personalization (UMAP), 2015.
    Optimal and Effective Web Service Composition with Trust and User Preference
    H. Wang, B. Zou, G. Guo, J. Zhang, Z. Yang
    Proceedings of the 22nd IEEE International Conference on Web Services (ICWS, acceptance rate: 20%), pp. 329-336, 2015.
    ETAF: An Extended Trust Antecedents Framework for Trust Prediction[PDF, Slides]
    G. Guo, J. Zhang, D. Thalmann, N. Yorke-Smith
    Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM, acceptance rate: 18%), pp. 540-547, 2014.
    From Ratings to Trust: an Empirical Study of Implicit Trust in Recommender Systems[PDF, Slides]
    G. Guo, J. Zhang, D. Thalmann, A. Basu, N. Yorke-Smith
    Proceedings of the 29th ACM Symposium on Applied Computing (SAC), pp. 248-253, 2014.
    Privacy Preserving Trusted Social Feedback[PDF]
    A. Basu, J.C. Corena, S. Kiyomoto, S. Marsh, J. Vaidya, G. Guo, J. Zhang, Y. Miyake
    Proceedings of the 29th ACM Symposium on Applied Computing (SAC), pp. 1706-1711, 2014.
    A Hybrid Recommender System based on Material Concepts with Difficulty Levels[PDF]
    G. Guo, M. H. Anjorin, B. S. Lee
    Proceedings of the 21st International Conference on Computers in Education (ICCE), pp. 90-96, 2013.
    Combining Recommender and Reputation Systems to Produce Better Online Advice[PDF]
    A. Josang, G. Guo, M. S. Pini, F. Santini, Y. Xu
    Proceedings of the 10th International Conference on Modeling Decisions for Artificial Intelligence (MDAI), pp. 126-138, 2013.
    Prior Ratings: A New Information Source for Recommender Systems in E-Commerce[PDF, Poster]
    G. Guo, J. Zhang, D. Thalmann, N. Yorke-Smith
    Proceedings of the 7th ACM Conference on Recommender Systems (RecSys), pp. 383-386, 2013.
    Integrating Trust and Similarity to Ameliorate the Data Sparsity and Cold Start for Recommender Systems[PDF, Slides]
    G. Guo
    Proceedings of the 7th ACM Conference on Recommender Systems (RecSys), pp. 451-454, 2013.
    A Novel Bayesian Similarity Measure for Recommender Systems[PDF, Poster, Code]
    G. Guo, J. Zhang, N. Yorke-Smith
    Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), pp. 2619-2625, 2013.
    Note: Although the code is dependent on an obsolete package, the implementation logic may be still useful for some others. We may integrate the Bayesian similarity into LibRec some time.
    Improving the Performance of Recommender Systems by Alleviating the Data Sparsity and Cold Start Problems[PDF]
    G. Guo
    Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), pp. 3217-3218, 2013.
    A New Recommender System for 3D E-Commerce: An EEG Based Approach[PDF]
    G. Guo, M. Elgendi
    Proceedings of the 2013 International Conference on Innovation and Information Management (ICIIM), pp. 61-65, 2013.
    A Simple but Effective Method to Incorporate Trusted Neighbors in Recommender Systems[PDF]
    G. Guo, J. Zhang, D. Thalmann
    Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization (UMAP), pp. 114-125, 2012.
    Resolving Data Sparsity and Cold Start in Recommender Systems[PDF]
    G. Guo
    Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization (UMAP), pp. 361-364, 2012.
    WCP-nets: A Weighted Extension to CP-nets for Web Service Selection[PDF]
    H. Wang, J. Zhang, W. Sun, H. Song, G. Guo, X. Zhou
    Proceedings of the 10th International Conference on Service Oriented Computing (ICSOC), pp. 298-312, 2012.
    Service Selection based on Similarity Measurement for Conditional Qualitative Preference[PDF]
    H. Wang, J. Zhang, H. Wang, Y. Tang, G. Guo
    Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 612-619, 2012.
    Improving PGP Web of Trust through the Expansion of Trusted Neighborhood[PDF, Slides]
    G. Guo, J. Zhang, J. Vassileva
    Proceedings of the 2011 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 489-494, 2011.

Workshops

    IFUP: Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization
    F. Zhu, Y. Zhang, N. Yorke-Smith, G. Guo, X. Chen
    Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM), pp. 804-805, 2018.
    Preface: Papers and Research from the 2016 International Workshop IFUP[PDF]
    G. Guo, R. Burke, F. Zhu, N. Yorke-Smith
    Proceedings of the 2016 International Workshop on Multi-dimension Information Fusion for User Modeling and Personalisation (UMAP IFUP), 2016.
    Reducing Information Overload in Social Networks through Streamlined Presentation: a Study of Content-centric and Person-centric Contexts[PDF]
    R. Cohen, N. Sardana, K. Rahim, D.Y. Lam, M. Li, O. Maccarthy, E. Woo, G. Guo
    Proceedings of the 3rd International Workshop on Incentives and Trust in E-Commerce (AAAI WIT-EC), 2014.
    Qualitative and Quantitative Preferences Based Web Service Composition by Integrating with Trust
    H. Wang, B. Zou, G. Guo
    Proceedings of the 2nd International Workshop on Incentives and Trust in E-Commerce (IJCAI WIT-EC), 2013.
    Recommending Messages to Users in Social Networks: a Cross-Site Study[PDF]
    R. Cohen, N. Sardana, K. Rahim, D. Y. Lam, M. Li, O. Maccarthy, E. Woo, J. Zhang, G. Guo
    Proceedings of the 12th International Conference on Machine Learning and Applications (ICMLA Bigdata), pp. 445-450, 2013.

Reports

    Deep Learning-based Sequential Recommender Systems: Concepts, Algorithms, and Evaluations
    H. Fang, G. Guo, D. Zhang, Y. Shu
    Tutorial, The 19th International Conference on Web Engineering (ICWE), 2019.
    Exploiting Ratings and Trust to Resolve the Data Sparsity and Cold Start of Recommender Systems
    G. Guo
    Ph.D Thesis, School of Computer Engineering, Nanyang Technological University, 2014.