Module overview
Aims and Objectives
Learning Outcomes
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
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- The auctions used in online advertising
- Concepts and example applications from social computing, including crowdsourcing, recommender systems, and online auctions
- Applications in crowdsourcing
- Incentives in crowdsourcing applications
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
Type | Hours |
---|---|
Wider reading or practice | 28 |
Revision | 10 |
Preparation for scheduled sessions | 18 |
Follow-up work | 18 |
Lecture | 36 |
Completion of assessment task | 40 |
Total study time | 150 |
Resources & Reading list
Textbooks
Jeff Howe. Crowdsourcing: How the Power of the Crowd is Driving the Future of Business.
Charu C. Aggarwal (2016). Recommender Systems: The Textbook. Springer.
Jon Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. David Easley.
Tim Ash, Maura Ginty. Landing Page Optimization: The Definitive Guide to Testing and Tuning for Conversions. Rich Page.
Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich. Recommender systems: an introduction.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
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.
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.
Method | Percentage contribution |
---|---|
Examination | 100% |
Repeat Information
Repeat type: Internal & External