dynamic generation of personalized hybrid recommender systems
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Dynamic Generation of Personalized Hybrid Recommender Systems
Simon Dooms
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About …
Simon Dooms PhD Student Ghent University Belgium
FeedbackResultsLearningFrameworkIntro
2009 2010 2014
Research Personal PhD Grant (4 years)
October, 2013
RatingsDatasetsOnline FeedbackOnline experimentsDistributed recsysHybrid recsysUser-centric
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Information Overload
FeedbackResultsLearningFrameworkIntro
Collaborative Filtering
Content-based FilteringItemAttributeKNN
Content-based Filtering
FactorWiseMatrixFactorization
BiasedMatrixFactorization
MatrixfactorizationItemKNN
Random Items
Popular Items
SigmoidSVDPlusPlus
SigmoidCombinedAsymmetricFactorModel
SigmoidItemAsymmetricFactorModel
SigmoidUserAsymmetricFactorModel GlobalAverage
ItemAverage
SVDPlusPlus
TimeAwareBaselineWithFrequencies
CoClusteringSlopeOne
UserItemBaseline
UserKNN
BiPolarSlopeOne
NaiveBayes TimeAwareBaseline
Probability-based Extended Profile Filtering
LatentFeatureLogLinearModelSVD
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Recommendation Algorithm Overload
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What about Hybrids? Combine the merits!BUT Typical Hybrid: CB + CF Manual algorithm selection, static configuration
FeedbackResultsLearningFrameworkIntro
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Our Goal
Generate personalized hybrid recommender systems
Research Questions– Do all users benefit from personalized hybrid recommenders?– How automatically adapt a hybrid recommender?– How evaluate the system?– How respond to real-time online user feedback?
FeedbackResultsLearningFrameworkIntro
Automatically
Different for every user
Involving many different algorithms
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A Recommender FrameworkFeedbackResultsLearningFrameworkIntro
MovieTweetings
MyMediaLite
Python Code
HTML front-end
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Learning module –an optimization problem–
FeedbackResultsLearningFrameworkIntro
Optimize weights such that is minimized.
How evaluate ? On what data?
Objective function: Evaluation metric (e.g., RMSE, MAE, …)
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Optimization parametersFeedbackResultsLearningFrameworkIntro
Training TestFold 1
Training TestFold 2
Training TestFold 3
Ratings for 1 user
Recommendation algorithms:
8
User algorithm weights:
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Optimize
FeedbackResultsLearningFrameworkIntro
Slow (hours) Fast (seconds)
Fol
d da
tase
tsA
ll da
ta
9
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Results (offline)
Experimented with Switching strategies A genetic algorithm Binary search tree
Statistically significant results, journal article: “Offline Optimization for User-specific hybrid Recommender Systems”
FeedbackResultsLearningFrameworkIntro
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Demo
Online TOUR Offline TOUR
FeedbackResultsLearningFrameworkIntro
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FeedbackResultsLearningFrameworkIntro
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CFF: Call For Feedback
Interesting topics include, but are not limited to Optimization suggestions Offline evaluation strategy Online evaluation strategy Interesting integration ideas
FeedbackResultsLearningFrameworkIntro
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Dynamic Generation of Personalized Hybrid Recommender Systems
Simon Dooms