1. Collaborative Data Analysis and Multi-Agent Systems Robert W. Thomas CSCE 824 15 APR 2013

2. Agenda Problem Description Existing Research Overview Limitation of Existing Results Future Research Suggestions 2

3. Problem Description Information Overload Divide and Conquer; Reconcile Recommender Systems and Social Media – Content Filtering – Collaborative Filtering – Collaborative Data Analysis through Agents 3

4. Content Filtering Recommendations based on items similar to what has been preferred previously 4

5. Collaborative Filtering (CF) Recommendations based on what others in a network prefer Different Techniques – Memory-Based – Model-Based – Hybrid 5

6. Memory-Based CF Similarity Computation Prediction and Recommendation Computation Top-N Recommendations 6

7. Similarity Computation 7

8. Prediction and Recommendation Computation 8

9. Top-N Recommendations Item-Based User-Based 9

10. Model-Based CF Bayesian Belief Net Clustering Regression-Based Markov Decision Process (MDP) –Based Latent Semantic 10

11. Bayesian Belief Net 11

12. Clustering Cluster: collection of similar objects, dissimilar to objects in other clusters – Pearson correlation can be used Three Categories – Partitioning – Density-based – Hierarchal Often an Intermediate Step 12

13. Regression-Based Use approximation of ratings to make predictions against a regression model Apply to situations where rating vectors have large Euclidean distances but very high Similarity Computation scores 13

14. MDP-Based Sequential Optimization Problem – S = {states} – A = {actions} – R = {rewards} for r(s,a,s’) – Pr = {transition probabilities} for pr(s,a,s’) Partially Observable MDP (POMDP) 14

15. Latent Semantic Uses statistical modeling to discover additional communities or profiles 15

16. Network Trust We’re all mad here; I’m mad; you’re mad. Opinions of different contacts are valued more than others under certain conditions Accounting for this can increase CF accuracy Semantic Knowledge Social Tie-Strength 16

17. Hybrid CF CF + Content-Based CF + CF CF + CF and/or Content-Based 17

18. Limitations of Existing Solutions Time / Accuracy Trade Offs Noisy Data Data Sparsity (New User) Scalability Synonymy Gray Sheep Shilling Attacks Privacy 18

19. Future Research Suggestions Hybrids Semantics Trust Parallel Processing – Multi-Agent Systems 19

20. BACKUP 20

21. References Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in Artificial Intelligence 2009 (2009): 4. Chen, Wei, and Simon Fong. "Social network collaborative filtering framework and online trust factors: a case study on Facebook." Digital Information Management (ICDIM), 2010 Fifth International Conference on. IEEE, 2010. O'Donovan, John, and Barry Smyth. "Trust in recommender systems." Proceedings of the 10th international conference on Intelligent user interfaces. ACM, 2005. 21