Social Computing Techniques

Learning Outcomes

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

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
• The auctions used in online advertising
• 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

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

Summative

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

Referral

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

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.