Collaborative Data Analysis and Multi-Agent Systems

1. Collaborative Data Analysisand 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

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

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

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

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

7. Similarity Computation Two users: u,v Two items: i,j = items both u and v have rated = avg rating of co-rated items of the user = users who rated both i and j = avg rating of the item by those users R = m x n user-item matrix are n dimensional vectors corresponding to i and j column of R • Compares Users or Items • Correlation-Based (Pearson correlation) • Vector Cosine-Based

8. Prediction and Recommendation Computation • Weighted Sum of Others’ Ratings • Simple Weighted Average Prediction P for active user a, on item i = avg rating of user u = weight between user a and user u = users who rated item i Prediction P for user u on item i = all other rated items for user u = weight between items i and n = rating for user u on item n

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

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

11. Bayesian Belief Net • Bayesian logic – decision making and inferential statistics • Simple Bayesian • Memory-Based • Laplace Estimator to avoid a conditional probability of 0 • Tree Augmented naïve Bayes and naïve Bayes optimized by Extended Logic Regression (ELR) • Require extended training periods to produce results beyond simple Bayesian and Pearson correlation

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

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

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

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

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

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

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

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

20. BACKUP

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.