Recommender Systems
Affiliation:
Harding University
Subject:
Recommender Systems
Topic or Subject area. "When you enter text, you will see suggested topics":
Collective Intelligence
Web Science
Type of resource -- presentation, etc.:
Slides
Contributor First Name:
Frank
Contributor Last Name:
McCown
Contributer Email:
fmccown@harding.edu
Creators: separate names with a comma:
Frank McCown
Description:
These slides introduce recommender systems, focusing on details of how collaborative filtering can be used to recommend products or show what products are like a given product. It concludes with some discussion of the Netflix Prize and issues around releasing anonymized data.
Education Level:
Undergraduate (Lower Division)
Undergraduate (Upper Division)
| Attachment | Size |
|---|---|
| Recommender-Systems.pptx | 3.25 MB |
Copyrights:
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License 