Lectures
Lecture 1. Introduction
Lecture 2. Introduction to Data Mining and Preliminaries
Recommended preparation:
Completing the Part 2 of your assignment before the lecture
Reading Chapter 4 of Sifa's profiling book
Reading Section 7.4 of data mining book of Han et. al
Lecture 3. Affinity Mining
Recommended preparation :
Completing Part 1 of your assignment before the lecture
Chapter 1 and Sections 5.1 and 5.2.1 of the second edition of the Data Mining book from Han et al.
Lecture 4. Latent Pattern Mining I: Introduction
Recommended preparation :
Chapters 2 and 3 of Matrix and Tensor Factorization for Profiling Player Behavior from Sifa
Lecture 5: Latent Pattern Mining II: Algorithms for Unconstrained Factorizations
Recommended preparation :
Chapter 3 of Matrix and Tensor Factorization for Profiling Player Behavior from Sifa
Lecture 6: Latent Pattern Mining III: Recommender Systems
Recommended preparation :
Chapter 10 of Matrix and Tensor Factorization for Profiling Player Behavior from Sifa
Sifa, Rafet, et al. “Matrix and Tensor Factorization Based Game Content Recommender Systems: A Bottom-Up Architecture and a Comparative Online Evaluation.” Fourteenth Artificial Intelligence and Interactive Digital Entertainment Conference. 2018. (Download from https://tinyurl.com/y5tdwlfl)
Lecture 7: Latent Pattern Mining IV: Profiling User Behavior with Constrained Models
Recommended preparation :
Sifa, Rafet, Anders Drachen, and Christian Bauckhage. Profiling in Games: Understanding Behavior from Telemetry. Social Interactions in Virtual Worlds: An Interdisciplinary Perspective (2018). (available under)
Chapters 4 and 5 of Matrix and Tensor Factorization for Profiling Player Behavior from Sifa
Lecture 8: Latent Pattern Mining V: Factorizing Proximity Data
Recommended preparation :
Chapter 8 of Matrix and Tensor Factorization for Profiling Player Behavior from Sifa
Lecture 9: Introduction to Text Mining
Recommended preparation :
Chapters 2 and 6 of the Information Retrieval (2009) book of Manning et al.
Lectures 10 & 11: Predictive Data Mining for Media Applications
Recommended preparation :
Chapter 6 in the second edition of Negnevitsky’s book.
Rafet Sifa, Anders Drachen, Florian Block, Anisha Dubhashi, Spencer Seongjae Moon, Hao Xiao, Zili Li, Diego Klabjan, Simon Demediuk. “Archetypal Analysis Based Anomaly Detection for Improved Storytelling in Multiplayer Online Battle Arena Games.” In Proceedings of the Australasian Computer Science Week Multiconference, ACM, 2021. URL
Slides: