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The University of Southampton

COMP3208 Social Computing Techniques

Module Overview

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • Concepts and example applications from social computing, including crowdsourcing, recommender systems, and online auctions
  • Incentives in crowdsourcing applications
  • Applications in crowdsourcing
  • The auctions used in online advertising
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Use recommender technologies such as item-based and user-based collaborative filtering techniques
  • Describe the most important techniques and issues in designing, building and modelling social computing systems
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Set up social computing experiments and analyse the results using a scientific approach


Crowdsourcing - Human computation - Citizen science - Amazon Mechanical Turk and other platforms - Incentive engineering Reputation and recommender systems - User-based collaborative filtering - Item-based collaborative filtering Online auctions - Sponsored search - Display advertising Web analytics and experimental design - A/B split testing - Latin squares Rank aggregation

Learning and Teaching

Completion of assessment task40
Follow-up work18
Wider reading or practice28
Preparation for scheduled sessions18
Total study time150

Resources & Reading list

Jeff Howe. Crowdsourcing: How the Power of the Crowd is Driving the Future of Business. 

Jon Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. 

Charu C. Aggarwal (2016). Recommender Systems: The Textbook. 

Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich. Recommender systems: an introduction. 

Tim Ash, Maura Ginty. Landing Page Optimization: The Definitive Guide to Testing and Tuning for Conversions. 



MethodPercentage contribution
Examination  (1.5 hours) 60%
Implementation and Analysis 40%


MethodPercentage contribution
Examination 100%


MethodPercentage contribution
Examination  (2 hours) 100%

Repeat Information

Repeat type: Internal & External

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