Estimating probabilities in recommendation systems

Estimating Probabilities in Recommendation Systems

Modeling ranked data is an essential component in a number of important applications including recommendation systems and websearch. In many cases, judges omit preference among unobserved items and between unobserved and observed items. This case of analyzing incomplete rankings is very important from a practical perspective and yet has not been fully studied due to considerable computational d...

Estimating Probabilities in PCFGs

Estimating Probabilities in PCFGs

◮ find P̂(N j → ζ) = C(N j→ζ) ∑ γ C(N j→γ) ◮ C(X) = count of how often rule X is used ◮ no annotation ⇒ no rule counts! =̂ hidden data problem – similar to Hidden Markov Models ◮ start with some initial rule probabilities, parse training sentences, use parse probabilities as indicator of confidence ◮ find expectation of how often a rule is used ◮ based on these expectations, maximize probabilities:

Estimating Paired-Interaction Probabilities

CHAPTER 2 Estimating Probabilities

This is a rough draft chapter intended for inclusion in the upcoming second edition of the textbook Machine Learning, T.M. Mitchell, McGraw Hill. You are welcome to use this for educational purposes, but do not duplicate or repost it on the internet. For online copies of this and other materials related to this book, visit the web site www.cs.cmu.edu/∼tom/mlbook.html. Please send suggestions fo...