Reading List

Text books

Machine Learning

Lectures on Machine Learning by Hyeong In Choi, Seoul National University, Department of Mathematics

Deep Learning

Dive into Deep Learning by Zhang, Aston, et al.
Deep learning by Goodfellow, Ian, Yoshua Bengio, and Aaron Courville

Recommender System

Recommender-Systems: The Textbook by Charu C. Aggarwal

Outlier Detection

Outlier Analysis by Charu C. Aggarwal

Papers

Recommender System

Survey
- Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey). Knowledge-based systems, 46, 109-132.
Collaborative Filtering
- Matrix Factorization
  - Survey
    - Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
  - Lecture Note
    - CS168: The Modern Algorithmic Toolbox Lecture #9: The Singular Value Decomposition (SVD) and Low-Rank Matrix Approximations
    - CME 323: Distributed Algorithms and Optimization, Spring 2015 Matrix Completion via Alternating Least Square(ALS)
  - Methods
    - Alternative Least Square (ALS)
      - Bell, R. M., & Koren, Y. (2007, October). Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In Seventh IEEE international conference on data mining (ICDM 2007) (pp. 43-52). IEEE.
    - Non-negative Matrix Factorization
      - Gillis, N. (2017). Introduction to nonnegative matrix factorization. arXiv preprint arXiv:1703.00663.
    - Probabilistic Matrix Factorization
      - Mnih, A., & Salakhutdinov, R. R. (2007). Probabilistic matrix factorization. Advances in neural information processing systems, 20.
    - Temporal Dynamics
      - Koren, Y. (2009, June). Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 447-456).
    - Neural Collaborative Filtering
      - He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173-182).
    - Factorization Machines (FM)
      - Rendle, S. (2010, December). Factorization machines. In 2010 IEEE International conference on data mining (pp. 995-1000). IEEE.
    - Deep Factorization Machines (DeepFM)
      - Guo, H., Tang, R., Ye, Y., Li, Z., & He, X. (2017). DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247.
Evaluation
- Ge, M., Delgado-Battenfeld, C., & Jannach, D. (2010, September). Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Recommender systems (pp. 257-260).
- Kotkov, D., Wang, S., & Veijalainen, J. (2016). A survey of serendipity in recommender systems. Knowledge-Based Systems, 111, 180-192.

Educational Data Mining

Survey
- Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning analytics (pp. 61-75). Springer, New York, NY.
Knowledge Tracing (answer correctness prediction)
- Survey
  - Pelánek, R. (2017). Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques. User Modeling and User-Adapted Interaction, 27(3), 313-350.
  - Gervet, T., Koedinger, K., Schneider, J., & Mitchell, T. (2020). When is deep learning the best approach to knowledge tracing?. Journal of Educational Data Mining, 12(3), 31-54.
- Bayesian Knowledge Tracing (BKT)
  - Lecture Notes
    - CS229: Hiden Markov Models Fundamentals by Daniel Ramage
    - Hiden Markov Models by Fran ̧cois Caron
  - Methods
    - Yudelson, M. V., Koedinger, K. R., & Gordon, G. J. (2013, July). Individualized bayesian knowledge tracing models. In International conference on artificial intelligence in education (pp. 171-180). Springer, Berlin, Heidelberg.
  - Review
    - van De Sande, B. (2013). Properties of the Bayesian Knowledge Tracing Model. Journal of Educational Data Mining, 5(2), 1-10.
  - Implementation
    - pyBKT
      - Badrinath, A., Wang, F., & Pardos, Z. (2021). pyBKT: an accessible python library of Bayesian knowledge tracing models. arXiv preprint arXiv:2105.00385.
- Deep Knowledge Tracing (DKT)
  - Methods
    - DKT
      - Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. Advances in neural information processing systems, 28.
    - DKT+
      - Yeung, C. K., & Yeung, D. Y. (2018, June). Addressing two problems in deep knowledge tracing via prediction-consistent regularization. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale (pp. 1-10).
    - PEBG
      - Liu, Y., Yang, Y., Chen, X., Shen, J., Zhang, H., & Yu, Y. (2020). Improving knowledge tracing via pre-training question embeddings. arXiv preprint arXiv:2012.05031.
    - DKVMN
      - Zhang, J., Shi, X., King, I., & Yeung, D. Y. (2017, April). Dynamic key-value memory networks for knowledge tracing. In Proceedings of the 26th international conference on World Wide Web (pp. 765-774).
    - Deep-IRT
      - Yeung, C. K. (2019). Deep-IRT: Make deep learning based knowledge tracing explainable using item response theory. arXiv preprint arXiv:1904.11738. Chicago
    - SAKT
      - Pandey, S., & Karypis, G. (2019). A self-attentive model for knowledge tracing. arXiv preprint arXiv:1907.06837.
  - Review
    - Khajah, M., Lindsey, R. V., & Mozer, M. C. (2016). How deep is knowledge tracing?. arXiv preprint arXiv:1604.02416.
    - Xiong, X., Zhao, S., Van Inwegen, E. G., & Beck, J. E. (2016). Going deeper with deep knowledge tracing. International Educational Data Mining Society.
- Logistic Knowledge Tracing (LKT)
  - Methods
    - LFA (Learning Factor Analysis)
      - Cen, H., Koedinger, K., & Junker, B. (2006, June). Learning factors analysis–a general method for cognitive model evaluation and improvement. In International conference on intelligent tutoring systems (pp. 164-175). Springer, Berlin, Heidelberg.
    - PFA (Performance Factor Analysis)
      - Pavlik Jr, P. I., Cen, H., & Koedinger, K. R. (2009). Performance Factors Analysis--A New Alternative to Knowledge Tracing. Online Submission.
    - R-PFA
      - Galyardt, A., & Goldin, I. (2015). Move Your Lamp Post: Recent Data Reflects Learner Knowledge Better than Older Data. Journal of Educational Data Mining, 7(2), 83-108.
    - LKT (Logistic Knowledge Tracing)
      - Pavlik, P. I., Eglington, L. G., & Harrell-Williams, L. M. (2021). Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling. IEEE Transactions on Learning Technologies, 14(5), 624-639.
    - KTM (Knowledge Tracing Machine)
      - Vie, J. J., & Kashima, H. (2019, July). Knowledge tracing machines: Factorization machines for knowledge tracing. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 750-757).
    - DAS3H
      - Choffin, B., Popineau, F., Bourda, Y., & Vie, J. J. (2019). DAS3H: modeling student learning and forgetting for optimally scheduling distributed practice of skills. arXiv preprint arXiv:1905.06873.
  - Review
    - Gong, Y., Beck, J. E., & Heffernan, N. T. (2011). How to construct more accurate student models: Comparing and optimizing knowledge tracing and performance factor analysis. International Journal of Artificial Intelligence in Education, 21(1-2), 27-46.
    - Rosé, C. P., McLaughlin, E. A., Liu, R., & Koedinger, K. R. (2019). Explanatory learner models: Why machine learning (alone) is not the answer. British Journal of Educational Technology, 50(6), 2943-2958.
    - Wu, M., Davis, R. L., Domingue, B. W., Piech, C., & Goodman, N. (2020). Variational item response theory: Fast, accurate, and expressive. arXiv preprint arXiv:2002.00276.
Exercise Recommendation
- Survey
  - Nabizadeh, A. H., Leal, J. P., Rafsanjani, H. N., & Shah, R. R. (2020). Learning path personalization and recommendation methods: A survey of the state-of-the-art. Expert Systems with Applications, 159, 113596.
- Methods
  - Wu, Z., Li, M., Tang, Y., & Liang, Q. (2020). Exercise recommendation based on knowledge concept prediction. Knowledge-Based Systems, 210, 106481.
  - Ai, F., Chen, Y., Guo, Y., Zhao, Y., Wang, Z., Fu, G., & Wang, G. (2019). Concept-Aware Deep Knowledge Tracing and Exercise Recommendation in an Online Learning System. International Educational Data Mining Society.
  - Cai, D., Zhang, Y., & Dai, B. (2019, December). Learning path recommendation based on knowledge tracing model and reinforcement learning. In 2019 IEEE 5th International Conference on Computer and Communications (ICCC) (pp. 1881-1885). IEEE.
- Lecture Notes
  - Combinatorial Optimization
    - Simulated Annealing Overview by Zak Varty

Outlier Detection

Survey
- Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58.
Unsupervised Learning Methods
- Methods
  - LOF
    - Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May). LOF: identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (pp. 93-104).
  - Isolation Forest
    - Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008, December). Isolation forest. In 2008 eighth ieee international conference on data mining (pp. 413-422). IEEE.
- Use of Outlier Scores
  - Gao, J., & Tan, P. N. (2006, December). Converting output scores from outlier detection algorithms into probability estimates. In Sixth International Conference on Data Mining (ICDM'06) (pp. 212-221). IEEE.
  - Schubert, E., Wojdanowski, R., Zimek, A., & Kriegel, H. P. (2012, April). On evaluation of outlier rankings and outlier scores. In Proceedings of the 2012 SIAM International Conference on Data Mining (pp. 1047-1058). Society for Industrial and Applied Mathematics.
  - Kriegel, H. P., Kroger, P., Schubert, E., & Zimek, A. (2011, April). Interpreting and unifying outlier scores. In Proceedings of the 2011 SIAM International Conference on Data Mining (pp. 13-24). Society for Industrial and Applied Mathematics.
- For High-Dimensional Data
  - Survey
    - Zimek, A., Schubert, E., & Kriegel, H. P. (2012). A survey on unsupervised outlier detection in high‐dimensional numerical data. Statistical Analysis and Data Mining: The ASA Data Science Journal, 5(5), 363-387.
    - Campos, G. O., Zimek, A., Sander, J., Campello, R. J., Micenková, B., Schubert, E., ... & Houle, M. E. (2016). On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data mining and knowledge discovery, 30(4), 891-927. Chicago
    - Xu, X., Liu, H., Li, L., & Yao, M. (2018). A comparison of outlier detection techniques for high-dimensional data. International Journal of Computational Intelligence Systems, 11(1), 652.
  - Concentration Effect (Motivation for HD Outlier detection)
    - Beyer, K., Goldstein, J., Ramakrishnan, R., & Shaft, U. (1999, January). When is “nearest neighbor” meaningful?. In International conference on database theory (pp. 217-235). Springer, Berlin, Heidelberg.
    - Durrant, R. J., & Kabán, A. (2009). When is ‘nearest neighbour’meaningful: A converse theorem and implications. Journal of Complexity, 25(4), 385-397.

etc

Hyper-parameter Tuning

Survey
- Yu, T., & Zhu, H. (2020). Hyper-parameter optimization: A review of algorithms and applications. arXiv preprint arXiv:2003.05689.

Model Combination

Motivation
- Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts?
Review
- Model Combination in multiclass classification
Methods
- Stacked Generalization
  - Issues in Stacked Generalization

Ordinal Classification

Methods
- Frank, E., & Hall, M. (2001, September). A simple approach to ordinal classification. In European conference on machine learning (pp. 145-156). Springer, Berlin, Heidelberg.

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Machine Learning

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Outlier Detection

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Recommender System

Educational Data Mining

Outlier Detection

etc

Hyper-parameter Tuning

Model Combination

Ordinal Classification

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Machine Learning

Deep Learning

Recommender System

Outlier Detection

Papers

Recommender System

Educational Data Mining

Outlier Detection

etc

Hyper-parameter Tuning

Model Combination

Ordinal Classification

Websites

Soft Readings (in Korean)

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