COMP5331: Knowledge Discovery in Databases

Project

Important Dates

• Group Forming deadline: 25 Sept 10:00am
• Proposal deadline: 2 Oct 10:30am (in class)
• Final report deadline: 18 Nov 10:30am (in class)
• PPT/Source Code submission deadline: on your presentation date (23:59)
• Presentation Dates: TBA

No. of Students

Exactly Three or Four (if you want to form a group with 1-2 members, please obtain the instructor's approval. No five-member group is allowed.)

Please send an email to our TA, Tianwen CHEN (tchenaj@connect.ust.hk), with email title "COMP5331: Group Forming" for your group information. Our TA will assign a group ID to your group.

• Information of each member of your group
  • Student ID
  • Student name

If you do not know some classmates in this class and would like us to form a group for you, please also send an email to our TA with the same email title. In the email, state that you want us to form a group for you. But, it is NOT guaranteed that we will form a complete group for you. Besides, we do not guarantee that the teammates assigned to you in the group must be responsible and must be hard-working. You should need to take a risk on this "random" teammate assignment.

Proposal

Please write a proposal including the following items.

• A specific topic (or title) for this project
• Type of this project: Survey/Implementation/Research
• Group No.
• Information of each member of your group
  • Student ID
  • Student name
  • Your research/FYP supervisor (if any)
  • Your own research/FYP topic (if any) and an explanation why this project is different from your own research topic
  • A declaration statement that this project is done solely within the course but not other scopes (e.g., other courses and research projects)
• A brief description about this project (about 1000~2000 words)
• A list of papers to be read in this project

Final Report

Please write a final report including the following items.

• A specific topic (or title) for this project
• Type of this project: Survey/Implementation/Research
• Group No.
• Information of each member of your group
  • Student ID
  • Student name
  • Your research/FYP supervisor (if any)
  • Your own research/FYP topic (if any) and an explanation why this project is different from your own research topic
  • A declaration statement that this project is done solely within the course but not other scopes (e.g., other courses and research projects)
• Content
  • Number of words
    • Survey Type
      • about 5,000~10,000 words
    • Implementation Type
      • about 1,000~10,000 words
    • Research Type
      • about 5,000~10,000 words
    • For each type,
      • if you like, you can write in more words with the instructor's approval
  • Guideline
    • Write a normal report (e.g., Introduction, Related Work, Algorithm, Conclusion, References, ...)
    • Write an additional section called "Contribution". The number of words in this section does not count towards to the total number of words specified above
      • For each member in the group, please write a sub-section with about 500 words stating your contributions to the project (e.g., what you have done for this project)

Group List

Presentation

Requirement:

• For a 4-member group, each member must give at least 4 minutes and at most 5 minutes for the entire presentation.
  Thus, the duration of the entire presentation is at least 16 minutes and at most 20 minutes.
• For a 3-member group, each member must give at least 4 minutes and at most 6.66 minutes for the entire presentation.
  Thus, the duration of the entire presentation is at least 12 minutes and at most 20 minutes.
• For a 2-member group, each member must give at least 4 minutes and at most 10 minutes for the entire presentation.
  Thus, the duration of the entire presentation is at least 8 minutes and at most 20 minutes.
• For a 1-member group, each member must give at least 4 minutes and at most 20 minutes for the entire presentation.
  Thus, the duration of the entire presentation is at least 4 minutes and at most 20 minutes.
  
  
• An over-time presentation may lead to mark deductions. Thus, please time your presentation.
• Each group will be assigned to a task to ask some questions to another group (on the day of presentation). Each member in this group must ask at least one question for the other group.
• Please fill in and print this file before you come to the presentation.
  

Submission:

• Each group must need to hand in a PPT file on the presentation date. If your group has some source code files, please also submit them to us. In this case, please zip your PPT file with your source files. In your source files, please write a readme file which includes the following.
  1. how to compile
  2. how to execute
  3. the description of each source file
  4. an example to show how to run the program
  5. the operating system you tested your program (e.g., linux and Windows)
  6. anything you want to include
• The PPT/Source Code submission deadline is your presentation date (23:59) .
• Please send the zipped file to our instructor directly.

Suggested Topics

You can select any topics you want. The following topics are suggested to those students who have no idea about knowledge discovery in databases.

• For those who have no idea, you can select one of the following topics.
  If you select a topic, please think a specific topic (or title) under this topic for your project which is related to the papers you chose in this topic. You can select any papers under a topic. After that, you can also find the related works/papers for these papers as the paper list of your project.
• For those who have some ideas, you can find any papers by yourself and propose your own specific topic in this project. You can choose any papers from data mining conferences (such as KDD, ICDM and SDM), databases conferences (such as SIGMOD, VLDB and ICDE), machine learning conferences (such as NIPS, AAAI and ICML) and IR conferences (such as SIGIR). Please ask for an instructor's approval to include your selected papers in your project.
• The following papers are chosen from KDD and ICDM, which does not mean that you must read the papers from these conferences. In fact, there are many other data mining papers which appear in databases conferences, machine learning conferences and IR conferences. You can select the papers from these conferences for the project.

Topic 1: Social Network

• Qingyun Wu, Zhige Li, Huazheng Wang, Wei Chen, Hongning Wang
  Factorization Bandits for Online Influence Maximization
  KDD 2019 (pdf)
• Pinghua Xu, Wenbin Hu, Jia Wu, Bo Du
  Link Prediction with Signed Latent Factors in Signed Social Networks
  KDD 2019 (pdf)
• Haoyang Li, Peng Cui, Chengxi Zang, Tianyang Zhang, Wenwu Zhu, Yishi Lin
  Fates of Microscopic Social Ecosystems: Keep Alive or Dead?
  KDD 2019 (pdf)
• Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, Wen Su
  MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network
  KDD 2019 (pdf)
• Liang Zhang, Keli Xiao, Hengshu Zhu, Chuanren Liu, Jingyuan Yang, and Bo Jin
  CADEN: A Context-Aware Deep Embedding Network for Financial Opinions Mining
  ICDM 2018 (pdf)
• Antonis Matakos and Aristides Gionis
  Tell me something my friends do not know: Diversity maximization in social networks
  ICDM 2018 (pdf)
• Yong Luo, Huaizheng Zhang, Yongjie Wang, Yonggang Wen, and Xinwen Zhang
  ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment
  ICDM 2018 (pdf)
• Yunfei Lu, Linyun Yu, Tianyang Zhang, Chengxi Zang, Peng Cui, Chaoming Song, and Wenwu Zhu
  Collective Human Behavior in Cascading System: Discovery, Modeling and Applications
  ICDM 2018 (pdf)

Topic 2: Pattern Mining

• Ari Kobren, Barna Saha, Andrew McCallum
  Paper Matching with Local Fairness Constraints
  KDD 2019 (pdf)
• Panagiotis Mandros, Mario Boley, and Jilles Vreeken
  Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms
  ICDM 2018 (pdf)
• Sacha ServanSchreiber, Matteo Riondato, and Emanuel Zgraggen
  ProSecCo: Progressive Sequence Mining with Convergence Guarantees
  ICDM 2018 (pdf)
• Keqian Li, Hanwen Zha, Yu Su, and Xifeng Yan
  Concept Mining via Embedding
  ICDM 2018 (pdf)
  

Topic 3: Data Stream

• Yanhao Wang, Yuchen Li, Kian-Lee Tan
  Coresets for Minimum Enclosing Balls over Sliding Windows
  KDD 2019 (pdf)
• Pinghui Wang, Yiyan Qi, Yuanming Zhang, Qiaozhu Zhai, Chenxu Wang, John C.S. Lui, Xiaohong Guan
  A Memory-Efficient Sketch Method for Estimating High Similarities in Streaming Sets
  KDD 2019 (pdf)
• Yishuai Du, Yimin Zheng, Kuang-chih Lee, and Shandian Zhe
  Probabilistic Streaming Tensor Decomposition
  ICDM 2018 (pdf)
• Shan You, Chang Xu, and Chao Xu
  Online Dictionary Learning with Confidence
  ICDM 2018 (pdf)
  
• He Huang, Bokai Cao, Philip S. Yu, Chang-Dong Wang, and Alex D. Leow
  dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction
  ICDM 2018 (pdf)
  

Topic 4: Spatial Mining

• Parikshit Ram, Kaushik Sinha
  Revisiting kd-tree for Nearest Neighbor Search
  KDD 2019 (pdf)
• Yue Cui, Liwei Deng, Yan Zhao, Bin Yao, Vincent W. Zheng, Kai Zheng
  Hidden POI Ranking with Spatial Crowdsourcing
  KDD 2019 (pdf)
• Zheyi Pan, Yuxuan Liang, Weifeng Wang , Yong Yu, Yu Zheng, Junbo Zhang
  Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning
  KDD 2019 (pdf)
• Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Xinran Tong, Hui Xiong
  Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network
  KDD 2019 (pdf)
• Qi Wang, Pang-Ning Tan, and Jiayu Zhou
  Imputing Structured Missing Values in Spatial Data with Clustered Adversarial Matrix Factorization
  ICDM 2018 (pdf)
• Fei Yi, Zhiwen Yu, Fuzhen Zhuang, Xiao Zhang, Bin Guo, and Hui Xiong
  An Integrated Model for Crime Prediction Using Temporal and Spatial Factors
  ICDM 2018 (pdf)
  
  

Topic 5: Graph Mining

• Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos
  Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
  KDD 2019 (pdf)
• Daniel Zügner, Stephan Günnemann
  Certifiable Robustness and Robust Training for Graph Convolutional Networks
  KDD 2019 (pdf)
  
• Hongchang Gao, Jian Pei, Heng Huang
  ProGAN: Network Embedding via Proximity Generative Adversarial Network
  KDD 2019 (pdf)
• Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu Aggarwal
  Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
  KDD 2019 (pdf)
• Yuan Yin, Zhewei Wei
  Scalable Graph Embeddings via Sparse Transpose Proximities
  KDD 2019 (pdf)
  
• Sheng Guan, Hanchao Ma, Yinghui Wu
  Attribute-Driven Backbone Discovery
  KDD 2019 (pdf)
  
• Songgaojun Deng, Huzefa Rangwala, Yue Ning
  Learning Dynamic Context Graphs for Predicting Social Events
  KDD 2019 (pdf)
• Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu
  Robust Graph Convolutional Networks Against Adversarial Attacks
  KDD 2019 (pdf)
  
• Hongyang Gao, Shuiwang Ji
  Graph Representation Learning via Hard and Channel-Wise Attention Networks
  KDD 2019 (pdf)
• Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson
  Graph-based Semi-Supervised & Active Learning for Edge Flows
  KDD 2019 (pdf)
• Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Qili Zhu
  Exact-K Recommendation via Maximal Clique Optimization
  KDD 2019 (pdf)
• Yang Zhou, Sixing Wu, Chao Jiang, Zijie Zhang, Dejing Dou, Ruoming Jin, and Pengwei Wang
  Density-adaptive Local Edge Representation Learning with Generative Adversarial Network Multi-label Edge Classification
  ICDM 2018 (pdf)
• Carl Yang, Yichen Feng, Pan Li, Yu Shi, and Jiawei Han
  Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
  ICDM 2018 (pdf)
• Russell Reas, Stephen Ash, Robert Barton, and Andrew Borthwick
  SuperPart: Supervised graph partitioning for record linkage
  ICDM 2018 (pdf)
• Xi Zhang, Jingyuan Chou, and Fei Wang
  Integrative Analysis of Patient Health Records and Neuroimages via Memory-based Graph Convolutional Network
  ICDM 2018 (pdf)
  
• Tyler Wilson, Pang-Ning Tan, and Lifeng Luo
  A Low Rank Weighted Graph Convolutional Approach to Weather Prediction
  ICDM 2018 (pdf)
  

Topic 6: Representation Learning

• Hanpeng Liu, Yaguang Li, Michael Tsang, Yan Liu
  CoSTCo: A Neural Tensor Completion Model for Sparse Tensors
  KDD 2019 (pdf)
• Junheng Hao, Muhao Chen, Wenchao Yu, Yizhou Sun, Wei Wang
  Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts
  KDD 2019 (pdf)
• Qi Liu, Shiwei Tong, Chuanren Liu, Hongke Zhao, Enhong Chen, Haiping Ma, Shijin Wang
  Exploiting Cognitive Structure for Adaptive Learning
  KDD 2019 (pdf)
• Changping Meng, Jiasen Yang, Bruno Ribeiro, Jennifer Neville
  HATS: A Hierarchical Sequence-Attention Framework for Inductive Set-of-Sets Embeddings
  KDD 2019 (pdf)
• Daheng Wang, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang
  TUBE: Embedding Behavior Outcomes for Predicting Success
  KDD 2019 (pdf)
• Yang Zhou, Sixing Wu, Chao Jiang, Zijie Zhang, Dejing Dou, Ruoming Jin, and Pengwei Wang
  Density-adaptive Local Edge Representation Learning with Generative Adversarial Network Multi-label Edge Classification
  ICDM 2018 (pdf)
• Fei Jiang, Lei Zheng, Jin Xu, and Philip S. Yu
  FI-GRL: Fast Inductive Graph Representation Learning via Projection-Cost Preservation
  ICDM 2018 (pdf)
• Charlie Soh, Annamalai Narayanan, Lihui Chen, Yang Liu and Lipo Wang
  Apk2vec: Semi-Supervised Multi-view Representation Learning for Profiling Android Applications
  ICDM 2018 (pdf)
• Adelene Sim and Andrew Borthwick
  Record2Vec: Unsupervised Representation Learning for Structured Records
  ICDM 2018 (pdf)
• Jiaye Wu, Yang Wang, Peng Wang, Jian Pei, and Wei Wang
  Finding Maximal Significant Linear Representation between Long Time Series
  ICDM 2018 (pdf)

Topic 7: Interpretability

• Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama
  Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining
  KDD 2019 (pdf)
• Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana
  Axiomatic Interpretability for Multiclass Additive Models
  KDD 2019 (pdf)
• Weiyu Cheng, Yanyan Shen, Linpeng Huang, Yanmin Zhu
  Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis
  KDD 2019 (pdf)
• Yunzhe Jia, James Bailey, Kotagiri Ramamohanarao, Christopher Leckie, Michael E. Houle
  Improving the Quality of Explanations with Local Embedding Perturbations
  KDD 2019 (pdf)
• Zhiqiang Tao, Sheng Li, Zhaowen Wang, Chen Fang, Longqi Yang, Handong Zhao, Yun Fu
  Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit
  KDD 2019 (pdf)
• Fan Yang, Ninghao Liu, Suhang Wang, and Xia Hu
  Towards Interpretation of Recommender Systems with Sorted Explanation Paths
  ICDM 2018 (pdf)
• Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, and Xing Xie
  A Reinforcement Learning Framework for Explainable Recommendation
  ICDM 2018 (pdf)

Topic 8: Temporal Mining

• Kan Ren, Jiarui Qin, Lei Zheng, Zhengyu Yang, Weinan Zhang, Yong Yu
  Deep Landscape Forecasting for Real-time Bidding Advertising
  KDD 2019 (pdf)
  
• Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, Lujia Pan
  Predicting Path Failure In Time-Evolving Graphs
  KDD 2019 (pdf)
  
• Lucas Maystre, Victor Kristof, Matthias Grossglauser
  Pairwise Comparisons with Flexible Time-Dynamics
  KDD 2019 (pdf)
• Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He
  Modeling Extreme Events in Time Series Prediction
  KDD 2019 (pdf)
• Pengyang Wang, Yanjie Fu, Hui Xiong, Xiaolin li
  Adversarial Substructured Representation Learning for Mobile User Profiling
  KDD 2019 (pdf)
• Srijan Kumar, Xikun Zhang, Jure Leskovec
  Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
  KDD 2019 (pdf)
• Lisi Chen, Shuo Shang, Christian S. Jensen, Bin Yao, Zhiwei Zhang, Ling Shao
  Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation
  KDD 2019 (pdf)
• Min-hwan Oh, Garud Iyengar
  Sequential Anomaly Detection using Inverse Reinforcement Learning
  KDD 2019 (pdf)
• Yuanduo He, Jialiang Pei, Xu Chu, Yasha Wang, Zhu Jin, and Guangju Peng
  Characteristic Subspace Learning for Time Series Classification
  ICDM 2018 (pdf)
• Ye Yuan, Guangxu Xun, Fenglong Ma, Yaqing Wang, Nan Du, Kebin Jia, Lu Su, and Aidong Zhang
  MuVAN: A Multi-view Attention Network for Multivariate Temporal Data
  ICDM 2018 (pdf)
  
• Isak Karlsson, Jonathan Rebane, Panagiotis Papapetrou, and Aristides Gionis
  Explainable Time Series Tweaking via Irreversible and Reversible Temporal Transformations
  ICDM 2018 (pdf)
  
• Kyongmin Yeo, Igor Melnyk, and Nam Nguyen
  DE-RNN: Forecasting the probability density function of nonlinear time series
  ICDM 2018 (pdf)
  
• Polina Rozenshtein, Francesco Bonchi, Aristides Gionis, Mauro Sozio, and Nikolaj Tatti
  Finding events in temporal networks: Segmentation meets densest-subgraph discovery
  ICDM 2018 (pdf)

Topic 9: Deep Learning

• Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh
  Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
  KDD 2019 (pdf)
• Fei Tan, Zhi Wei, Jun He, Xiang Wu, Bo Peng, Haoran Liu, and Zhenyu Yan
  A blended deep learning approach for predicting user intended actions
  ICDM 2018 (pdf)
• Tong Chen, Hongzhi Yin, Hongxu Chen, Lin Wu, Hao Wang, Xiaofang Zhou, and Xue Li
  TADA: Trend Alignment with Dual-Attention Multi-Task Recurrent Neural Networks for Sales Prediction
  ICDM 2018 (pdf)
• Kai Shu, Suhang Wang, Thai Le, Dongwon Lee, and Huan Liu
  Deep Headline Generation for Clickbait Detection
  ICDM 2018 (pdf)
  
• Xiaolong Gong, Linpeng Huang, and Fuwei Wang
  Deep Semantic Correlation Learning based Hashing for Multimedia Cross-Modal Retrieval
  ICDM 2018 (pdf)
  
• Haibo Wang, Chuan Zhou, Jia Wu, Weizhen Dang, Xingquan Zhu, and Jilong Wang
  Deep Structure Learning for Fraud Detection
  ICDM 2018 (pdf)
• Xujiang Zhao, Feng Chen, and Jin-Hee Cho
  Deep Learning based Scalable Inference of Uncertain Opinions
  ICDM 2018 (pdf)
  
• Hao Wang, Enhong Chen, Qi Liu, Tong Xu, and Dongfang Du
  A United Approach to Learning Sparse Attributed Network Embedding
  ICDM 2018 (pdf)

Topic 10: Classification

• Ahmed Rashed,Josif Grabocka, Lars Schmidt-Thieme
  Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings
  KDD 2019 (pdf)
• Yujun Yan, Jiong Zhu, Marlena Duda, Eric Solarz, Chandra Sripada, Danai Koutra
  GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data
  KDD 2019 (pdf)
  
• Chuanhai Zhang, Wallapak Tavanapong, Gavin Kijkul, Johnny Wong, Piet C. de Groen, and JungHwan Oh
  Similarity-based Active Learning for Image Classification under Class Imbalance
  ICDM 2018 (pdf)
  
  

Topic 11: Rule/Causality Mining

• Alexander Marx, Jilles Vreeken,
  Identifiability of Cause and Effect using Regularized Regression
  KDD 2019 (pdf)
• Xuan Yin, Liangjie Hong
  The Identification and Estimation of Direct and Indirect Effects in Online A/B Tests through Causal Mediation Analysis
  KDD 2019 (pdf)
• Altobelli Mantuan and Leandro Fernandes
  Spatial Contextualization for Closed Itemset Mining
  ICDM 2018 (pdf)
• Lamine Diop, Cheikh Talibouya Diop, Arnaud Giacometti, Dominique Li, and Arnaud Soulet
  Sequential Pattern Sampling with Norm Constraints
  ICDM 2018 (pdf)
  

Topic 12: Clustering

• Nicholas Monath, Ari Kobren , Akshay Krishnamurthy, Michael Glass, Andrew McCallum
  Scalable Hierarchical Clustering via Tree Grafting
  KDD 2019 (pdf)
• Feiping Nie, Cheng-Long Wang, Xuelong Li
  K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters
  KDD 2019 (pdf)
• Yuchao Liu, Ery Arias-Castro
  A Multiscale Scan Statistic for Adaptive Submatrix Localization
  KDD 2019 (pdf)
• Yaqiang Yao, Huanhuan Chen
  Robust Task Grouping with Representative Tasks for Clustered Multi-Task Learning
  KDD 2019 (pdf)
• Cong Fu, Yonghui Zhang, Deng Cai, Xiang Ren
  AtSNE: Efficient and Robust Visualization on GPU through Hierarchical Optimization
  KDD 2019 (pdf)
  
• Benjamin Schelling and Claudia Plant
  DipTransformation: Enhancing the Structure of a Dataset and thereby improving Clustering
  ICDM 2018 (pdf)
  
• Sebastian Buschj√§ger, Kuan-Hsun Chen, Jian-Jia Chen, and Katharina Morik
  Realization of Random Forest for Real-Time Evaluation through Tree Framing
  ICDM 2018 (pdf)
• Vincent Vercruyssen, Wannes Meert, Gust Verbruggen, Koen Maes, Ruben B√§umer, and Jesse Davis
  Semi-supervised anomaly detection with an application to water analytics
  ICDM 2018 (pdf)
  
• Houssam Zenati, Manon Romain, ChuanSheng Foo, Bruno Lecouat, and Vijay Chandrasekhar
  Adversarially Learned Anomaly Detection
  ICDM 2018 (pdf)
  

Topic 13: Recommendation System

• Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang
  Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
  KDD 2019 (pdf)
• Wang-Cheng Kang and Julian McAuley
  Self-Attentive Sequential Recommendation
  ICDM 2018 (pdf)
• Jiawei Chen, Can Wang, Martin Ester, Qihao Shi, Yan Feng, and Chun Chen
  Social Recommendation with Missing Not at Random Data
  ICDM 2018 (pdf)
• Shan-Yun Teng, Jundong Li,Lo Pang-Yun Ting, Kun-Ta Chuang, Huan Liu
  Interactive Unknowns Recommendation in E-Learning Systems
  ICDM 2018 (pdf)
• Yingpeng Du, Hongzhi Liu, Zhonghai Wu, and Xing Zhang
  Hierarchical Hybrid Feature Model For Top-N Context-Aware Recommendation
  ICDM 2018 (pdf)

Topic 14: Privacy

• Yi Li, Wei Xu
  PrivPy: General and Scalable Privacy-Preserving Data Mining
  KDD 2019 (pdf)
  
• Congzheng Song, Vitaly Shmatikov
  Auditing Data Provenance in Text-Generation Models
  KDD 2019 (pdf)
• Hao Zou, Kun Kuang, Boqi Chen, Peixuan Chen, Peng Cui
  Focused Context Balancing for Robust Offline Policy Evaluation
  KDD 2019 (pdf)
• Jiaxing Shen, Oren Lederman, Jiannong Cao, Florian Berg, Shaojie Tang, and Alex Pentland
  GINA: Group Gender Identification Using Privacy-Sensitive Audio Data
  ICDM 2018 (pdf)
  
• Thilina Ranbaduge and Peter Christen
  Privacy-Preserving Temporal Record Linkage
  ICDM 2018 (pdf)
  
• Qingxin Meng, Hengshu Zhu, Keli Xiao, and Hui Xiong
  Intelligent Salary Benchmarking for Talent Recruitment: A Holistic Matrix Factorization Approach
  ICDM 2018 (pdf)
  
• Wenbo Guo, Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xin, Lin Lin, Sui Huang, Xue Liu, and C. Lee Giles
  Defending Against Adversarial Samples Without Security through Obscurity
  ICDM 2018 (pdf)