collaborative data analysis and multi-agent systems
DESCRIPTION
Collaborative Data Analysis and Multi-Agent Systems. Robert W. Thomas CSCE 824 15 APR 2013. Agenda. Problem Description Existing Research Overview Limitation of Existing Results Future Research Suggestions. Problem Description. Information Overload Divide and Conquer; Reconcile - PowerPoint PPT PresentationTRANSCRIPT
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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.