Dynamic Generation of Personalized

Hybrid Recommender Systems

WiCa, Wireless & Cable, www.wica.intec.ugent.be Gaston Crommenlaan 8 box 201, 9050 Ghent, Belgium

Ghent University, Department of Information Technology

Simon Dooms simon.dooms@intec.ugent.be

Luc Martens luc.martens@intec.ugent.be

There is too much content available. Too

much to watch, listen to, or read it all. We

need automated intelligent content filtering

aka recommendation. The recommender

systems research domain spent the last 20

years actively developing and researching

new recommendation algorithms and

strategies, leading to a new problem …

MyMediaLite

Which one should I use?

Collaborative Filtering · Content-based Filtering · Knowledge-based Filtering M a t r i x f a c t o r i z a t i o n · F a c t o r W i s e M a t r i x F a c t o r i z a t i o n BiasedMatrixFactorization · Popular Items · Random Items · ItemKNN ItemAttributeKNN · CoClustering · TimeAwareBaselineWithFrequencies S V D P l u s P l u s · I t e m A v e r a g e · G l o b a l A v e r a g e SigmoidCombinedAsymmetricFactorModel · BiPolarSlopeOne · UserKNN UserItemBaseline · SlopeOne · SigmoidSVDPlusPlus · TimeAwareBaseline NaiveBayes · LatentFeatureLogLinearModel · SVD · PCA · Probability-based

?

Recommendation

Algorithm Overload

Information

Overload Goal

Hybrid recommender systems combine the

mer i ts o f mu l t ip le recommendat ion

algorithms but they are hard to configure

and usually include only a few algorithms.

What if we could throw all algorithms

together and have an intelligent system

automatically compose hybrids personalized

for each user? Well, … that’s the goal.

Hybrid Framework

Dataset

For our experiments we use the MovieLens

(1M) dataset. But since it lacks new and

recent items (most recent movie is from

2000), we merge it with a constantly growing

ratings dataset gathered from social media:

MovieTweetings https://github.com/sidooms/MovieTweetings

Algorithms are considered black boxes to

facilitate the integration of new and various

types of recommendation algorithms.

Currently we have integrated over 20

algorithms which include all of the rating

predictors from MyMediaLite and a few

custom algorithms. We plan on integrating

other recommendation libraries as well.

Algorithms

Learning = Optimization Problem

Recommendation algorithms

Algorithm weights vector 𝛾 𝑢 e.g. 𝛾 𝑢 = (1,0,0,0.5,1,1,0,1,0)

User-centric dynamic ensemble recommender 𝑔 𝑢, 𝑖 = 𝛾𝑎1

𝑢 ∗ 𝑔𝑎1𝑢, 𝑖 + 𝛾𝑎2

𝑢 ∗ 𝑔𝑎2𝑢, 𝑖 + … + 𝛾𝑎𝑛

𝑢 ∗ 𝑔𝑎𝑛𝑢, 𝑖

Optimize weights vector 𝛾 𝑢 = (𝛾𝑎1𝑢 , 𝛾𝑎2

𝑢 , … , 𝛾𝑎𝑛𝑢 )

such that 𝑓 𝛾 𝑢 is minimized.

Optimize

Fast (seconds) Slow (hours)

Responsive online recommender Real-time integration of new user ratings by adding the ratings to the fold test sets

Fold

data

sets

A

ll data

The Filter Bubble

Control and Transparency by allowing users

to view and modify their system-calculated

algorithm weights.

Offline Results

Early tests with RMSE evaluation on the

MovieLens (100K) dataset with 10

MyMediaLite algorithms, show statistically

interesting results. Future work: Online.