A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines

1. A non-IID Frameworkfor Collaborative Filtering with Restricted Boltzmann Machines KostadinGeorgiev, VMware Bulgaria Preslav Nakov, Qatar Computing Research Institute ICML, June 17, 2013, Atlanta

2. Non-IID framework for collaborative filtering • based on Restricted Botzmann Machines (RBMs) • with user ratings modeled as real values (vs. multinomials) • A neighborhood method boosted by RBM Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Overview

3. Collaborative filtering

4. Recommender systems • predict user preferences for new items • content-based vs. collaborative • Collaborative filtering (CF) • predictions inferred from the preferences of other users • N x M user-item matrix of rating values • large and highly sparse (e.g., 95% of values are missing) Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Introduction

5. User-based • most of the early CF systems • Item-based • e.g., (Sarwaret al., 2001) • Joint user-item based • matrix factorization, joint latent factor space • (Salakhutdinov& Mnih, 2008; Koren et al., 2009; Lawrence & Urtasun,2009); • probabilistic latent model • (Langseth& Nielsen, 2012) Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines User-based vs. Item-based CF

6. Restricted Botzmann Machines (Salakhutdinov et al., 2007) • user-based • ratings as multinomial variables • outperforms SVD • Unrestricted Boltzmann Machines (Truyen et al., 2009) • joint user-item based • connections between the visible units • preprocessing, correlations computation, neighborhood formation • ordinal modeling of ratings better than categorical Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Boltzmann Machines for CF

7. THIS work

8. User-based RBM (U-RBM) Item-based RBM (I-RBM) Hybrid non-IID RBM (UI-RBM) Neighborhood method boosted by I-RBM (I-RBM+INB) Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Outline

9. The visible layer • represents either all ratings by a user or all rating for an item • units model ratings as real values (vs. multinomial) • noise-free reconstruction is better Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines User/Item-based RBM Model

10. We remove the IID assumption for the training data Topology: Unit vij is connected to two independent hidden layers: one user-based and another item-based. Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Non-IID Hybrid RBM Model (1)

11. Missing values (ratings): the generatedpredictions are used during testing, but are ignored during training Training procedure: we average the predictions of the user-based and of the item-based RBM models Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Non-IID Hybrid RBM Model (2)

12. Use the I-RBM predictions from a neighborhood-based (NB) algorithm However, compute the averages from the original ratings Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Neighborhood Boosted by I-RBM

13. Experiments and Evaluation

14. Two MovieLensdatasets: • 100k: • 1,682 movies assigned • 943 users • 100,000 ratings • sparseness: 93.7% • 1M: • 3,952 movies • 6,040 users • 1 million ratings • sparseness: 95.8% • Each rating is an integer between 1 (worst) and 5 (best) Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Data

15. Mean Absolute Error (MAE): • Cross-validation • 5-fold • 80%:20% training:testingdata splits Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Evaluation

16. Evaluated three RBM-based models: • User-based RBM (U-RBM) • Item-based RBM (I-RBM) • Hybrid non-IID RBM model (UI-RBM) • Tested real-valued vs. multinomial visible units • for all above models • Neighborhood model boosted by I-RBM (I-RBM+INB) Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Experiments

17. Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Types of Visible Units: the IID Case In the IID case, multinomial visible units are better than real-valued.

18. Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Types of Visible Units: the non-IID Case In the non-IID case: real-valued visible units outperform multinomial.

19. Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Number of Units: the IID Case - Item-based RBM model outperforms user-based, but not by much. - Hybrid item-based RBM + NB model is relatively insensitive to number of units.

20. Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Results: MovieLens 100k

21. Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Results: MovieLens 1M

22. Conclusion and future work

23. Conclusion • proposed a non-IID RBM framework for CF • results rival the best CF algorithms, which are more complex • Future work • add an additional layer to model higher-order correlations • add content-based features, e.g., demographic Georgiev & Nakov: A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines Conclusion and Future Work Thank you!