1. StressSense: Detecting Stress in Unconstrained Acoustic Environments using Smartphones Hong Lu, Mashfiqui Rabbi, Gokul T. Chittaranjan, Denise Frauendorfer, Marianne Schmid Mast, Andrew T. Campbell, Daniel Gatica-Perez, Tanzeem Choudhury Intel Lab, Cornell University, EPFL, University of Neuchatel, Dartmouth College, Idiap and EPFL Ubicomp ‘12 2013-09-09 DJ Choi djchoi@mmlab.snu.ac.kr

2. 2/21 Content  Introduction Motivation Related work  Friendship-Interest Propagation (FIP) Model  Experiments  Conclusion

3. INTRODUCTION

4. 4/21 Motivation  In Online Social Network (OSN) Service, Interest targeting and Friendship Prediction are important  Many models for interest targeting and friendship prediction have been proposed, which utilize limited information Interest Targeting without the consideration of friendship Friendship Prediction without the consideration of user’s interest

5. 5/21 Contribution points  Proposing Friendship-Interest Propagation (FIP) model utilizing both friendship and interest information Several loss functions for flexibility Computational framework for scalability  Inspecting FIP model to real-OSN service, “Yahoo! Pulse” and verifying the model

6. BACKGROUND

7. 7/21 Interest targeting  Guessing user’s interest based on others’ behavior and interest information  Primary utilized models for calculation Sparse coding Collaborative Filtering (CF) models  Applications Recommendation service Selective advertisement

8. 8/21 Collaborative Filtering (CF) models  Definitions i and j imply an user and an item respectively  Neighborhood models Selecting users or items based on the similarity User i’s interest to the item j Similarity between user i and i’ User i’s interest toward item j

9. 9/21 CF models (cont’d)  Latent factor models Factorizing a matrix into user’s latent features and item’s latent features Regression based -Taking some observable features into account  Neighborhood based latent factor models User’s latent features Item’s latent features

10. 10/21 Friendship Prediction  Predicting the friends who a person may know  Primary utilized models for calculation Random Walk Spectral Algorithms Degree of user i Connection weight Unnormalized Laplacian ( L = D – S) where, D : diagonal maxtix, S : given network

11. FRIENDSHIP-INTEREST PROPAGATION MODEL

12. 12/21 Basic model  Coupled latent factor model of both user-user and user-item interaction  Encoding the two heterogeneous types of dyadic relationships simultaneously Interest targeting Friendship prediction

13. 13/21 Model specification  FIP is based on the latent factors model  The problem of minimizing the negative log- posterior of FIP boils down to the following objectives: Loss functions Regulation Penalizing complexity

14. 14/21 Loss function selection  For the flexibility check, several loss functions are used Least mean square Lazy least mean square Logistic regression Huber loss

15. 15/21 Bias Correction  It is typically unobservable that an user i is not interest in item j Absence of a preference statement or a social link does not mean negative information  FIP works with bias correction For positive (observed) entries, execute a stochastic gradient algorithm For negative (observed) entries, take some samples and consider those as negative example -Missing entries represent very weak negative instances

16. 16/21 Optimizations for implementation  Parallel stochastic gradient algorithm are executed [M.Zinkevich, M.Weimer, A.Smola, and L.Li, Parallelized Stochastic Gradient Descent. In NIPS’ 10]  Feature hashing is utilized to store most-needed latent factors in memory for efficient speed [K.Weinberger, A.Dasgupta, J.Langford, A.Smola, and J.Attenberg. Feature hashing for large scale multitask learning. In ICML ’09]  MapReduce Map phase: independently calculate and Reduce phase: combine the results

17. EXPERIMENTS

18. 18/21 Experiment setting  Yahoo Pulse (http://pulse.yahoo.com) datasethttp://pulse.yahoo.com 1.2M users 6.1M friend connections 29M interest interactions  Divides dataset into training and testing set randomly and executes cross-validation 5 times  Metrics Average Precision (AP) Average Recall (AR) Normalized Discounted Cumulative Gain (nDCG) -Normalized position-discounted precision score

19. 19/21 Experiment result  Similarity based Item oriented Model (SIM)  Regression & Neighborhood Latent Factor Model (RLFM, LRFM respectively)

20. CONCLUSION

21. 21/21 Conclusion  FIP model bridges collaborative filtering in recommendation system and random walk in social network analysis with a coupled latent factor model  In real social network service, recommendations based on FIP gives more precise lists

22. Discussion