PhD Defense: Dynamic Generation of Personalized Hybrid Recommender Systems

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Dynamic Generation of Personalized Hybrid Recommender Systems

Simon DoomsPublic PhD Presentation

December 19, 2014. Belgium.

News Movies

RestaurantsHotelsBooks

TV shows

Clothes

Apps Laptops

CultureBars Recipes

Comic books Perfume

Conferences

Websites

GamesColors

There’s too much of everything

Spotify’s music catalog contains 20 million songs

Every minute 100 hours of video uploaded to

YouTube

Every 5 minutes a new book on Amazon

We can’t have it all

News Movies

RestaurantsHotelsBooks

TV shows

Clothes

Apps Laptops

CultureBars Recipes

Comic books Perfume

Conferences

Websites

GamesColors

The Solution?

Recommender Systems

Content-based FilteringItemAttributeKNNFactorWiseMatrixFactorization

MatrixfactorizationItemKNN

SigmoidCombinedAsymmetricFactorModel

SigmoidItemAsymmetricFactorModel

SigmoidUserAsymmetricFactorModel GlobalAverage

ItemAverage

SVDPlusPlus

TimeAwareBaselineWithFrequencies

SlopeOneUserKNN

BiPolarSlopeOne

LatentFeatureLogLinearModel

SVD

Collaborative FilteringBiasedMatrixFactorization

Random Items

SigmoidSVDPlusPlus

CoClusteringUserItemBaseline

TimeAwareBaseline

Probability-based Extended Profile Filtering

Recommender Systems

Content-based FilteringItemAttributeKNNFactorWiseMatrixFactorization

MatrixfactorizationItemKNN

LatentFeatureLogLinearModel

SVD

Collaborative FilteringBiasedMatrixFactorization

Random Items

SigmoidSVDPlusPlusTimeAwareBaseline

Probability-based Extended Profile Filtering

AanbevelingssystemenWhat’s the challenge?

Content-based FilteringItemAttributeKNNFactorWiseMatrixFactorization

MatrixfactorizationItemKNN

LatentFeatureLogLinearModel

SVD

Collaborative FilteringBiasedMatrixFactorization

Random Items

SigmoidSVDPlusPlusTimeAwareBaseline

Probability-based Extended Profile Filtering

How to select the best system for a given context, user or domain?

Goal

Dynamic Generation of Personalized Hybrid Recommender Systems

The title

Goal

Dynamic Generation of Personalized Hybrid Recommender Systems

Goal

Dynamic Generation of Personalized Hybrid Recommender Systems

Goal

Dynamic Generation of Personalized Hybrid Recommender Systems

Goal

Dynamic Generation of Personalized Hybrid Recommender Systems

Goal

We Need Data

Products

Preferences

Data

MovieLens Netflix

(Old) Data is available

Data

MovieLens Netflix

1994 1995 1997

(Old) Data is available

Data

Old Data Old Recommendations

(Old) Data is available

but…

Data

+

Searching Recent Data

Data Searching Recent Data

Data

“I rated #IMDb”

Found it!

Data

Collected during 1 year, 9 months

320 000 ratings

MovieTweetings Dataset

30 000 users

20 000 movies

Found it!

Goal

Goal

+ + + +

Personalized

Personalized

Optimization Problem

Data

Optimization

Training EvaluateCombine

Data

Optimization

Training EvaluateCombine

Data

Optimization

Training EvaluateCombine

Data

Optimization

Training Evaluate

+

Combine

Repeat

Data

Optimization

Training Evaluate

+ = 25

= 50

= 75

Adapt

Combine

Optimize

Fold

dat

aset

s

Slow (hours)

All

dat

a

Fast (seconds)

Full Model

Optimize

Fold

dat

aset

s

Slow (hours)

All

dat

a

Fast (seconds)

New Ratings: no re-training required

Optimization Results

It works

… in theory

Real-life evaluation?

Goal

User Evaluation

Google Chrome Extension

Movie Recommendations

Recent Movies

Interaction

Recent Movies

Movie Recommendations

Interaction

Recent Movies

Movie Recommendations

Interaction

Recent Movies

Evaluation

Explicit

Movie Recommendations

Interaction

Recent Movies

Evaluation

Explicit

ImplicitClick Tracking & Logging

Movie Recommendations

Movie Recommendations: Results

Interactive users use the system more often

All users are different

Explicit & Implicit evaluation was positive

Goal Achieved

User Interface Design Experiments

But wait … there’s more!

High-Performance Calculation Research

User Interface Design Experiments

But wait … there’s more!

High-Performance Calculation Research

User Interface Design Experiments

But wait … there’s more!

High-Performance Calculation Research

User Interface Design Experiments

But wait … there’s more!

Rating systems, online experiments

High-Performance Calculation Research

User Interface Design Experiments

But wait … there’s more!

Conclusion

A dynamic, personalized hybrid recommender system works

All users are unique

Interactive systems are the future

Conclusion

You might also like…

My PhD dissertation for more details:

Google Scholar for my publications

Slideshare for my presentations and posters

Github for my public code and data

http://bit.ly/simonphd

https://github.com/sidooms

That’s all folks!

More questions? Contact me!

https://twitter.com/sidooms

simon [dot] dooms [at gmail dot] com

Dynamic Generation of Personalized Hybrid Recommender Systems

Simon DoomsPublic PhD Presentation

December 19, 2014. Belgium.