It’s possible to build powerful filtering systems by combining software and people, incorporating both algorithmic content analysis and human actions such as follow, share, and like. We’ll look recommendation systems, the Facebook news feed, and the socially-driven algorithms behind them. We’ll finish by looking at an example of using human preferences to drive machine learning algorithms: Google Web search.
Topics: Social filtering. The network structure of Twitter. Social software. Comment ranking on Reddit. Confidence sorting. User-item recommendation and collaborative filtering. Hybrid filters. What makes a good filter?
- Finding and Assessing Social Information Sources in the Context of Journalism, Nick Diakopolous et al.
- Item-Based Collaborative Filtering Recommendation Algorithms, Sarwar et. al
- How Reddit Ranking Algorithms Work, Amir Salihefendic
- Google News Personalization: Scalable Online Collaborative Filtering, Das et al
- Slashdot Moderation, Rob Malda
- What is Twitter, a Social Network or a News Media?, Haewoon Kwak, et al,
- The Netflix Prize, Wikipedia
- How does Google use human raters in web search?, Matt Cutts
Assignment: Hybrid filter Design. Design a filtering algorithm for status updates.