Recommender Systems-Machine Learning and Artificial Intelligence-Lecture Slides, Slides of Machine Learning

This lecture was delivered by Dr. Ramya Riya at Ankit Institute of Technology and Science. This lecture is part of lecture series on Machine Learning and Artificial Intelligence course. It includes: Recommender, System, Problem, Formulation, Predicting, Movie, Ratings, Content-based, Feature, vector, Optimization, Objective, Gradient, Descent

Typology: Slides

2011/2012

Uploaded on 08/26/2012

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Recommender

Systems

Problem

formula4on

Machine Learning

Example: Predic/ng movie ra/ngs

User rates movies using one to five stars

Movie Alice (1) Bob (2) Carol (3) Dave (4)

Love at last

Romance forever

Cute puppies of love

Nonstop car chases

Swords vs. karate

= no. users = no. movies = 1 if user has rated movie = ra4ng given by user to movie (defined only if )

Content-­‐based recommender systems

Movie Alice (1) Bob (2) Carol (3) Dave (4) (romance) (ac/on) Love at last 5 5 0 0 0.9 0 Romance forever 5?? 0 1.0 0. Cute puppies of love? 4 0? 0.99 0 Nonstop car chases 0 0 5 4 0.1 1. Swords vs. karate 0 0 5? 0 0.

For each user , learn a parameter. Predict user as

ra4ng movie with stars.

Problem formula/on

if user has rated movie (0 otherwise)

ra4ng by user on movie (if defined)

= parameter vector for user

= feature vector for movie

For user , movie , predicted ra4ng:

= no. of movies rated by user

To learn :

Op/miza/on algorithm:

Gradient descent update:

Recommender

Systems

Machine Learning

Collabora4ve

filtering

Problem mo/va/on Movie Alice (1) Bob (2) Carol (3) Dave (4) (romance) (ac/on) Love at last 5 5 0 0?? Romance forever 5?? 0?? Cute puppies of love

Nonstop car chases

Swords vs. karate 0 0 5???

Op/miza/on algorithm Given , to learn : Given , to learn :

Recommender

Systems

Machine Learning

Collabora4ve

filtering algorithm

Collabora/ve filtering op/miza/on objec/ve

Given , es4mate : Given , es4mate : Minimizing and simultaneously:

Recommender

Systems

Machine Learning

Vectoriza4on:

Low rank matrix

factoriza4on

Collabora/ve filtering

Movie Alice (1) Bob (2) Carol (3) Dave (4) Love at last 5 5 0 0 Romance forever 5?? 0 Cute puppies of love

Nonstop car chases

Swords vs. karate 0 0 5?

Finding related movies

For each product , we learn a feature vector.

How to find movies related to movie?

5 most similar movies to movie :

Find the 5 movies with the smallest.

Recommender

Systems

Machine Learning

Implementa4onal

detail: Mean

normaliza4on