Recommender Systems: Difference between revisions
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example: OWL - observed a word processor user and recommended shortcut keys based on their actions. | 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)