User Group Analytics
Slides of the tutorial
Half-day CIKM 2018 tutorial
Friday, 26 October 2018 09:00AM-10:30AM at Varsavia room
Tutorialists: Behrooz Omidvar-Tehrani, Sihem Amer-Yahia
Contact: firstname.lastname@univ-grenoble-alpes.fr
Embedded Files
Tutorial outline
Tutorial outline
Section 1. State of the Art in User Group Analytics (UGA)
Section 1. State of the Art in User Group Analytics (UGA)
Topic 1. Introduction to User Group Analytics (UGA): Motivations, Definitions, and Challenges
Topic 1. Introduction to User Group Analytics (UGA): Motivations, Definitions, and Challenges
- What is User Group Analytics (UGA)?
- What is user data and user groups?
- Real use cases in various application domains such as advertisement, program committee formation and quantified-self
- Overview of UGA challenges
Topic 2. UGA Components: Discovery, Exploration, and Visualization
Topic 2. UGA Components: Discovery, Exploration, and Visualization
- User group discovery: defining the group discovery process, and categorizing related work into attribute-based and action-based discovery
- User group exploration: defining the group exploration process, and categorizing related work into by-query, by-facet, by-example, and by-analytics exploration
- User group visualization: defining the building blocks of group visualization and mapping functions, and categorizing related work into graph-based and map-based visualization
Topic 3. UGA Evaluation: Challenges and Measures
Topic 3. UGA Evaluation: Challenges and Measures
- Challenges and measures for evaluating each UGA component
Section 2. New Challenges in Combining Components of User Group Analytics
Section 2. New Challenges in Combining Components of User Group Analytics
Topic 1. Combining Discovery and Visualization
Topic 1. Combining Discovery and Visualization
- Challenges of combining discovery and visualization
- Discussion of approaches for combining discovery and visualization in the following categories: distribution-based, facet-based, relation-based, and time-based
- Discussion of visualization approaches for representing discovered groups, such as self-organizing maps, belt charts, and regression heatmaps
Topic 2. Combining Discovery and Exploration
Topic 2. Combining Discovery and Exploration
- Challenges of combining discovery and exploration
- Discussion of approaches for combining discovery and exploration
- Discussion of different approaches of explicit and implicit feedback capture to enable exploratory discovery
Topic 3. Combining Exploration and Visualization
Topic 3. Combining Exploration and Visualization
- Challenges of combining exploration and visualization
- Discussion of approaches for combining exploration and visualization (aka, Visual Analytics)
Topic 4. All-in-One UGA: Challenges and Research Directions
Topic 4. All-in-One UGA: Challenges and Research Directions
- Benefits of building an all-in-one system where all UGA components are integrated
- Challenges of integrating UGA components
- Discussion of recently established literature of all-in-one UGA
- Discussion of future directions towards a full-fledged integration
Important References
Important References
Group Discovery
Group Discovery
- Mahashweta Das, Sihem Amer-Yahia, Gautam Das, and Cong Yu. MRI: meaningful interpretations of collaborative ratings. In PVLDB 2011.
- Jure Leskovec, Kevin J Lang, and Michael Mahoney. Empirical comparison of algorithms for network community detection. In WWW 2010.
- Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, and Prabhakar Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD 1998.
Group Exploration
Group Exploration
- Sihem Amer-Yahia, Sofia Kleisarchaki, Naresh Kumar Kolloju, Laks V. S. Lakshmanan, and Ruben H. Zamar. Exploring rated datasets with rating maps. In WWW 2017.
- Francesco Bonchi, Fosca Giannotti, Claudio Lucchese, Salvatore Orlando, Raffaele Perego, and Roberto Trasarti. Conquest: a constraint-based querying system for exploratory pattern discovery. In ICDE 2006.
- Ning Yan, Chengkai Li, Senjuti B Roy, Rakesh Ramegowda, and Gautam Das. Facetedpedia: enabling query-dependent faceted search for wikipedia. In CIKM 2010.
- Behrooz Omidvar-Tehrani, Sihem Amer-Yahia, and Alexandre Termier. Interactive user group analysis. In CIKM 2015.
Group Visualization
Group Visualization
- Jeffrey Heer and Joseph M Hellerstein. Tutorial on data visualization and social data analysis. In VLDB 2009.
- Ivan Herman, Guy Melançon, and M Scott Marshall. Graph visualization and navigation in information visualization: A survey. In TVCG 2000.
Combining Discovery and Visualization
Combining Discovery and Visualization
- Huiqi Xu, Zhen Li, Shumin Guo, and Keke Chen. Cloudvista: interactive and economical visual cluster analysis for big data in the cloud. In VLDB 2012.
- Josua Krause, Adam Perer, and Harry Stavropoulos. Supporting iterative cohort construction with visual temporal queries. In TVCG 2016.
Combining Discovery and Exploration
Combining Discovery and Exploration
- Arnab Nandi and HV Jagadish. Guided interaction: Rethinking the query-result paradigm. In VLDB 2011.
- Mario Boley, Michael Mampaey, Bo Kang, Pavel Tokmakov, and Stefan Wrobel. One click mining: Interactive local pattern discovery through implicit preference and performance learning. In IDEA Workshop in KDD 2013.
- Ioannis Arapakis, Mounia Lalmas, and George Valkanas. Understanding within-content engagement through pattern analysis of mouse gestures. In CIKM 2014.
Combining Exploration and Visualization
Combining Exploration and Visualization
- Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, and Jeffrey Heer. Vega-lite: A grammar of interactive graphics. In T VCG 2017.
- Tarique Siddiqui, Albert Kim, John Lee, Karrie Karahalios, and Aditya Parameswaran. Effortless data exploration with zenvisage: an expressive and interactive visual analytics system. In VLDB 2016.
All-in-One UGA
All-in-One UGA
- Çağatay Demiralp, Peter J Haas, Srinivasan Parthasarathy, and Tejaswini Pedapati. Foresight: recommending visual insights. In VLDB 2017.
- Eugene Wu, Leilani Battle, and Samuel R Madden. The case for data visualization management systems: vision paper. In VLDB 2014.
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