The University of Southampton
Courses

COMP3208 Social Computing Techniques

Module Overview

Aims and Objectives

Module Aims

The aim of this module is to introduce the fundamental concepts and computational techniques used in social computing. More specifically, in this module we will focus on three main areas: crowdsourcing, online auctions (including online advertising), and recommender systems. The module has a large practical component where you will learn how to solve a problem using crowdsourcing, and you will learn how to set up such experiments and dealing with incentives. In addition, you will learn about technologies such as auctions and algorithms for recommender systems. Note that this module does not cover social networking as this is covered elsewhere.

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 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
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

Syllabus

Crowdsourcing - Human computation - Participatory sensing - Citizen science - Amazon Mechanical Turk and other platforms Web analytics and Experimental design - A/B split testing - Latin squares Incentives and monetary payments in crowdsourcing Reputation systems - User-based collaborative filtering - Item-based collaborative filtering Online auctions - Sponsored search - Display advertising

Learning and Teaching

TypeHours
Revision10
Follow-up work18
Preparation for scheduled sessions18
Completion of assessment task18
Wider reading or practice50
Lecture36
Total study time150

Resources & Reading list

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

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

Don Tapscott and Anthony Williams. Wikinomics. 

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

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

Assessment

Summative

MethodPercentage contribution
Exam  (2 hours) 70%
Implementation and Analysis 30%

Referral

MethodPercentage contribution
Exam 100%

Repeat Information

Repeat type: Internal & External

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