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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. In a broad sense, social computing is about building computational systems that harness the collective intelligence of people, using techniques and insights from artificial intelligence and economics. More specifically, in this module we will focus on four main areas: crowdsourcing, online auctions (including online advertising), recommender systems and rank aggregation. The module has a large practical component where you will learn how to solve a real-world social computing problem and how to set up experiments in a principled manner to evaluate your system. In addition, you will learn about technologies such as auctions, algorithms for recommender systems, quality control mechanisms in crowdsourcing and incentive engineering. 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 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

Syllabus

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

TypeHours
Revision10
Completion of assessment task40
Lecture36
Wider reading or practice28
Follow-up work18
Preparation for scheduled sessions18
Total study time150

Resources & Reading list

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

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. 

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

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

Assessment

Summative

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

Repeat

MethodPercentage contribution
Examination 100%

Referral

MethodPercentage contribution
Examination  (2 hours) 100%

Repeat Information

Repeat type: Internal & External

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