Programming Collective Intelligence - Building Smart Web 2.0 Applications by Toby Segaran - ISBN 0596529325 - O'Reilly Media 2007
Clarify my understanding of machine learning, especially applied to the web.
Draw a schema (using PmGraphViz or another solution) of the situation of the area in the studied domain before having read the book.
- Chapter 1: Introduction to Collective Intelligence
- on the multiple usage with the classical example
- spam, Google, NetFlix, ...
- Chapter 2: Making recommendations
- defining Euclidean distance (p10) and Pearson correlation (p11)
- helpful 2D diagram of a "preference space" as a way to imagine in low dimensions
- Pearson correlation advantage of correcting for grade inflation
- pointing to other functions such as the Jaccard coefficient or Manhattan distance, cf Wikipedia:Metric &40;mathematics&41;#Examples
- explaining the profile base to the product base recommendation by transforming the matrix used until then with products per profile to profiles per product
- explaining item-based filtering
- exercises not done
Overall remarks and questions
So in the end, it was about X and was based on Y.
Point A, B and C are debatable because of e, f and j.
Draw a schema (using PmGraphViz or another solution) of the situation of the area in the studied domain after having read the book. Link it to the pre-reading model and align the two to help easy comparison.
Back to the Menu