Social Computing Techniques

Module overview

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.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

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

The auctions used in online advertising
Applications in crowdsourcing
Concepts and example applications from social computing, including crowdsourcing, recommender systems, and online auctions
Incentives in crowdsourcing applications

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

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

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

Learning and Teaching

Study time
Type	Hours
Lecture	36
Follow-up work	18
Revision	10
Wider reading or practice	28
Preparation for scheduled sessions	18
Completion of assessment task	40
Total study time	150

Resources & Reading list

Textbooks

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

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.

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

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

Assessment

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Implementation and Analysis	40%
Examination	60%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Examination	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Examination	100%

Repeat Information

Repeat type: Internal & External