LFD Book Forum Question on the Netflix example
 Register FAQ Calendar Mark Forums Read

#1
08-12-2012, 07:44 AM
 DeanS Member Join Date: Jul 2012 Posts: 16
Question on the Netflix example

So far in the course, we have always assumed that a human could determine the various factors which go into the solution. Eg. each movie has the dimensions for comedy, action, lead actor, etc. From these factors, we formed X and based on X, the data gave Ys and ultimately the weights.

However, there are areas where human experts cannot agree on what are the relevant factors. I wonder if there are algorithms where the computer determines the relevant factors from the data. Eg., the only data we have on movie rentals is the names of the customers, what movies they actually rented (possibly with a rating from 0 - 10 of their own like or dislike of the movie), and possibly what movies the declined to rent after having it suggested.

As an example, if I have a friend who I know generally likes the same movies I like, then if he/she rented a new movie and liked it, I would probably rent it regardless of any external classifications of factors.

Thanks.
#2
08-12-2012, 03:00 PM
 yaser Caltech Join Date: Aug 2009 Location: Pasadena, California, USA Posts: 1,478
Re: Question on the Netflix example

Quote:
 Originally Posted by DeanS So far in the course, we have always assumed that a human could determine the various factors which go into the solution. Eg. each movie has the dimensions for comedy, action, lead actor, etc. From these factors, we formed X and based on X, the data gave Ys and ultimately the weights. However, there are areas where human experts cannot agree on what are the relevant factors. I wonder if there are algorithms where the computer determines the relevant factors from the data. Eg., the only data we have on movie rentals is the names of the customers, what movies they actually rented (possibly with a rating from 0 - 10 of their own like or dislike of the movie), and possibly what movies the declined to rent after having it suggested. As an example, if I have a friend who I know generally likes the same movies I like, then if he/she rented a new movie and liked it, I would probably rent it regardless of any external classifications of factors.
What you are describing is precisely the algorithm given in the lecture. The meaning of the factors is not predetermined by humans. The factors in the algorithm are generic and initialized randomly. They only assume a "meaning" after training on ratings data. That meaning will not necessarily be along any coordinate that we humans understand. The comedy and other factors were only examples to make us understand how factors translate into ratings, but none of these meaningful factors is introduced in the algorithm.
__________________
Where everyone thinks alike, no one thinks very much

 Thread Tools Display Modes Linear Mode

 Posting Rules You may not post new threads You may not post replies You may not post attachments You may not edit your posts BB code is On Smilies are On [IMG] code is On HTML code is Off Forum Rules
 Forum Jump User Control Panel Private Messages Subscriptions Who's Online Search Forums Forums Home General     General Discussion of Machine Learning     Free Additional Material         Dynamic e-Chapters         Dynamic e-Appendices Course Discussions     Online LFD course         General comments on the course         Homework 1         Homework 2         Homework 3         Homework 4         Homework 5         Homework 6         Homework 7         Homework 8         The Final         Create New Homework Problems Book Feedback - Learning From Data     General comments on the book     Chapter 1 - The Learning Problem     Chapter 2 - Training versus Testing     Chapter 3 - The Linear Model     Chapter 4 - Overfitting     Chapter 5 - Three Learning Principles     e-Chapter 6 - Similarity Based Methods     e-Chapter 7 - Neural Networks     e-Chapter 8 - Support Vector Machines     e-Chapter 9 - Learning Aides     Appendix and Notation     e-Appendices

All times are GMT -7. The time now is 10:41 PM.