Recommender Systems: Difference between revisions

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Created page with "== Week 1 == === Introduction to Recommender Systems === You can invest time in indexing information. Indexing can be done by comparing term frequency with in a document compared..."
 
 
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=== Introduction to Recommender Systems ===
=== Introduction to Recommender Systems ===
You can invest time in indexing information. Indexing can be done by comparing term frequency with in a document compared to the corpus.
You can invest time in indexing information. Indexing can be done by comparing term frequency with in a document compared to the corpus.
History
* Invormation retrieval and filtering
* manual collaborative filtering
* automated collaborative filtering
* commercial era
=== Taxonomy of Recommender Systems ===
* Does sequence matter?
* Are we recommending new things? (an example of re-recommending, is Pandora)
A search engine is a recommender.
* What is the purpose of the recommendation? a sale, to inform
example: OWL - observed a word processor user and recommended shortcut keys based on their actions.
Personalization
* Generic (everyone gets the same recommendations)
* Demographic
* Ephemeral (matches current activity)
* Persistent (matches long-term interests)


== Footer ==
== Footer ==
[[Category: Notes]]
[[Category: Notes]]
[[Category: Course Work]]
[[Category: Course Work]]

Latest revision as of 14:28, 8 September 2013

Week 1

Introduction to Recommender Systems

You can invest time in indexing information. Indexing can be done by comparing term frequency with in a document compared to the corpus.

History

  • Invormation retrieval and filtering
  • manual collaborative filtering
  • automated collaborative filtering
  • commercial era

Taxonomy of Recommender Systems

  • Does sequence matter?
  • Are we recommending new things? (an example of re-recommending, is Pandora)

A search engine is a recommender.

  • What is the purpose of the recommendation? a sale, to inform

example: OWL - observed a word processor user and recommended shortcut keys based on their actions.

Personalization

  • Generic (everyone gets the same recommendations)
  • Demographic
  • Ephemeral (matches current activity)
  • Persistent (matches long-term interests)

Footer