Collaborative Data Analysisand Multi-Agent Systems

Robert W. ThomasCSCE 824

15 APR 2013

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Agenda

• Problem Description• Existing Research Overview• Limitation of Existing Results• Future Research Suggestions

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Problem Description

• Information Overload• Divide and Conquer; Reconcile• Recommender Systems and Social Media– Content Filtering– Collaborative Filtering– Collaborative Data Analysis through Agents

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Content Filtering

• Recommendations based on items similar to what has been preferred previously

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Collaborative Filtering (CF)

• Recommendations based on what others in a network prefer

• Different Techniques– Memory-Based– Model-Based– Hybrid

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Memory-Based CF

• Similarity Computation• Prediction and Recommendation Computation• Top-N Recommendations

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Similarity Computation

• Compares Users or Items• Correlation-Based (Pearson correlation)

• Vector Cosine-Based

Two users: u,vTwo 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

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Prediction and Recommendation Computation

• Weighted Sum of Others’ Ratings

• Simple Weighted AveragePrediction 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

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Top-N Recommendations

• Item-Based• User-Based

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Model-Based CF

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

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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

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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

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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

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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)

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Latent Semantic

• Uses statistical modeling to discover additional communities or profiles

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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

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Hybrid CF

• CF + Content-Based• CF + CF• CF + CF and/or Content-Based

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Limitations of Existing Solutions

• Time / Accuracy Trade Offs• Noisy Data• Data Sparsity (New User)• Scalability• Synonymy• Gray Sheep• Shilling Attacks• Privacy

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Future Research Suggestions

• Hybrids• Semantics• Trust• Parallel Processing– Multi-Agent Systems

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BACKUP

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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.