Ne present 01
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Revision as of 18:08, 30 January 2009 by Natasha
Introduction to Recommender Systems
- What is an RS?
- [What are different types of recommender systems]
Part 1: Exploring Recommendation Systems
- look at different recommender systems
- by dif companies
- dif methods
- historical vantage-point: start w/ oldest & come to newest
- what makes a good RS?
- other directions:
- AI/machine learning used in RS
- security/privacy issues?
- multi-agent systems, computer models for trust?
Part 2: Designing a Recommendation System?
- plan is to design new RS -- use novel topic (e.g. Bryn Mawr course recommendations) or technique (hybrid of existing techniques?)
- may deem infeasible or may prefer to explore RS research further
- in that case, will present analysis of current state of affairs re: recommendation systems & suggestions for further improvements
What I’ve Done So Far
- began reading articles re: RS
- lay articles including Jeffrey O'Brien's The race to create a 'smart' Google -- it's interesting; check it out!!
- began mathy article Gediminas Adomavicius & Alexander Tuzhilin's Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions
- set up Wiki page (notes, sources, thesis description)
- continue reading (& adding to wiki page)
- specifically, look into old/original rec systems
- want to know more about each of the three types of RS (personalized, social, item-based)
- more details on how they work
- look at companies -- how do they explain what they're doing (be wary of "we're so amazing"- type spin)
- any info on classifying/rating rec systems -- how do you know if one is "better" than another?)