An Introduction to Recommendation Engines
summary
An introduction to recommender engines covering basic principles, illustrative examples (including less obvious cases), and how a recommendation engine can help or harm a project. The talk references practical slides by Jeff Abrahamson hosted on GitHub and discusses applying recommender systems in real projects.
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chapters (10)
- 0:00Start
- 15:25Mathematics
- 17:05Cosine Similarity
- 21:18Content-Based Filtering
- 26:40Utility Matrix Symmetry
- 28:07Utility Matrix: Estimating μ
- 30:24Amazon: Item-to-Item Collaborative Filtering
- 37:51Slope One: Regression
- 38:41Slope One: Regression
- 39:55Specific Issues
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