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 [email protected]
Luc Martens [email protected]
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.
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