1 / 39

Recommender Systems

Recommender Systems. Jia-Bin Huang Virginia Tech. ECE-5424G / CS-5824. Spring 2019. Administrative. HW 4 due April 10. Unsupervised Learning. Clustering, K-Mean Expectation maximization Dimensionality reduction Anomaly detection Recommendation system.

floydw
Download Presentation

Recommender Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Recommender Systems Jia-Bin Huang Virginia Tech ECE-5424G / CS-5824 Spring 2019

  2. Administrative • HW 4 due April 10

  3. Unsupervised Learning • Clustering, K-Mean • Expectation maximization • Dimensionality reduction • Anomaly detection • Recommendation system

  4. Motivating example: Monitoring machines in a data center (Memory use) (CPU load) (Memory use) (CPU load)

  5. Multivariate Gaussian (normal) distribution • . Don’t model separately • Model all in one go. • Parameters: (covariance matrix)

  6. Multivariate Gaussian (normal) examples

  7. Multivariate Gaussian (normal) examples

  8. Multivariate Gaussian (normal) examples

  9. Anomaly detection using the multivariate Gaussian distribution • Fit model by setting 2 Give a new example , compute Flag an anomaly if

  10. Original model Automatically captures correlations between features Computationally more expensive Must have or else is non-invertible Original model Manually create features to capture anomalies where take unusual combinations of values Computationally cheaper (alternatively, scales better) OK even if training set size is small

  11. Recommender Systems • Motivation • Problem formulation • Content-based recommendations • Collaborative filtering • Mean normalization

  12. Recommender Systems • Motivation • Problem formulation • Content-based recommendations • Collaborative filtering • Mean normalization

  13. You may also like..?

  14. Recommender Systems • Motivation • Problem formulation • Content-based recommendations • Collaborative filtering • Mean normalization

  15. Example: Predicting movie ratings • User rates movies using zero to five stars • no. users • no. movies • if user has rated movie • rating given by user to movie

  16. Recommender Systems • Motivation • Problem formulation • Content-based recommendations • Collaborative filtering • Mean normalization

  17. Content-based recommender systems For each user , learn a parameter . Predict user as rating movie with stars.

  18. Content-based recommender systems For each user , learn a parameter . Predict user as rating movie with stars.

  19. Problem formulation • if user has rated movie • rating given by user to movie • parameter vector for user • feature vector for user • For each user predicted rating: • no. of movies rated by user j Goal: learn :

  20. Optimization objective • Learn (parameter for user ): Learn

  21. Optimization algorithm Gradient descent update: (for ) (for )

  22. Recommender Systems • Motivation • Problem formulation • Content-based recommendations • Collaborative filtering • Mean normalization

  23. Problem motivation

  24. Problem motivation

  25. Optimization algorithm • Given , to learn : • Given , to learn :

  26. Collaborative filtering • Given (and movie ratings), Can estimate • Given Can estimate

  27. Collaborative filtering optimization objective • Given , estimate • Given , estimate

  28. Collaborative filtering optimization objective • Given , estimate • Given , estimate • Minimize and simultaneously

  29. Collaborative filtering optimization objective

  30. Collaborative filtering algorithm • Initialize to small random values • Minimize using gradient descent (or an advanced optimization algorithm). For every • For a user with parameter and movie with (learned) feature , predict a star rating of

  31. Collaborative filtering

  32. Collaborative filtering • Predicted ratings: Low-rank matrix factorization

  33. Finding related movies/products • For each product , we learn a feature vector : romance, : action, : comedy, … • How to find movie relate to movie ? Small movie j and I are “similar”

  34. Recommender Systems • Motivation • Problem formulation • Content-based recommendations • Collaborative filtering • Mean normalization

  35. Users who have not rated any movies

  36. Users who have not rated any movies

  37. Mean normalization For user , on movie predict: User 5 (Eve): Learn

  38. Recommender Systems • Motivation • Problem formulation • Content-based recommendations • Collaborative filtering • Mean normalization

More Related