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Bayesian Personalized Ranking for Non-Uniformly Sampled Items

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The slide set describing our approach to the KDD Cup 2011, presented at the KDD Cup workshop in San Diego, California.

The slide set describing our approach to the KDD Cup 2011, presented at the KDD Cup workshop in San Diego, California.

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• 1. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items Bayesian Personalized Ranking for Non-Uniformly Sampled Items Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme University of Hildesheim 21 August 2011Zeno Gantner et al., University of Hildesheim 1 / 15
• 2. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items Questions (and Answers) What? Who? Which? How? Where? Why?Zeno Gantner et al., University of Hildesheim 2 / 15
• 3. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items Which problem to solve?Which problem to solve? Rating Prediction (Track 1) vs. Item Prediction (Track 2)Zeno Gantner et al., University of Hildesheim 3 / 15
• 4. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items How did we tackle the problem?How did we tackle the problem? Bayesian Personalized Ranking: 2 BPR(DS ) = argmax ln σ(ˆu,i (Θ) − ˆu,j (Θ) )−λ Θ s s Θ (u,i,j)∈DS DS contains all pairs of positive and negative items for each user, 1 σ(x) = 1+e −x is the logistic function, Θ represents the model parameters, ˆu,i (Θ) is the predicted score for user u and item i, and s λ Θ 2 is a regularization term to prevent overﬁtting. interpretation 1: reduce ranking to pairwise classif. [Balcan et al. 2008] interpretation 2: optimize for smoothed area under the ROC curve (AUC) Model: matrix factorization Learning: stochastic gradient ascent [Rendle et al., UAI 2009]Zeno Gantner et al., University of Hildesheim 4 / 15
• 5. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items How did we tackle the problem?How did we tackle the problem? 2 BPR(DS ) = argmax ln σ(ˆu,i − ˆu,j ) − λ Θ s s Θ (u,i,j)∈DS problem: all negative items j are given the same weightZeno Gantner et al., University of Hildesheim 5 / 15
• 6. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items How did we tackle the problem?How did we tackle the problem? 2 BPR(DS ) = argmax ln σ(ˆu,i − ˆu,j ) − λ Θ s s Θ (u,i,j)∈DS problem: all negative items j are given the same weight solution: adapt weights in the optimization criterion (and sampling probabilities in the learning algorithm) WBPR(DS ) = argmax wu wi wj ln σ(ˆu,i − ˆu,j ) − λ Θ 2 , s s Θ (u,i,j)∈DS where + wj = δ(j ∈ Iu ). (1) u∈UZeno Gantner et al., University of Hildesheim 5 / 15
• 7. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items Why did we not win?Why did we not win?But also: Why did we perform better than others? Why did we perform better than others? straightforward model that matches the prediction task pretty well scalability (e.g. k = 480 factors per user/item) integration of rating information (see paper) ensembles (see paper) Why did we not win? . . . two possible answers . . .Zeno Gantner et al., University of Hildesheim 6 / 15
• 8. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items Why did we not win?TaxonomyZeno Gantner et al., University of Hildesheim 7 / 15
• 9. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items Why did we not win?Learn the right contrast rating < 80 rating >= 80 liked? no rating rating >= 80 rated? no rating rating < 80 rating >= 80 ? no ratingZeno Gantner et al., University of Hildesheim 8 / 15
• 10. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items Why did we not win?Learn the right contrast rating < 80 rating >= 80 liked? no rating rating >= 80 rated? no rating rating < 80 rating >= 80 ? no ratingZeno Gantner et al., University of Hildesheim 9 / 15
• 11. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items Why did we not win?Learn the right contrast rating < 80 rating >= 80 liked? no rating rating >= 80 rated? no rating rating < 80 rating >= 80 ? no ratingZeno Gantner et al., University of Hildesheim 10 / 15
• 12. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items Why did we not win?Learn the right contrast rating < 80 rating >= 80 liked? no rating rating >= 80 rated? no rating rating < 80 rating >= 80 ? no ratingZeno Gantner et al., University of Hildesheim 11 / 15
• 13. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items Where?Where next? classiﬁcation → ranking → pairwise classiﬁcation pairwise classiﬁcation: try other losses, e.g. soft margin (hinge) loss Bayesian2 Personalized Ranking beyond KDD Cup: consider diﬀerent sampling schemes . . .Zeno Gantner et al., University of Hildesheim 12 / 15
• 14. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items SummarySummary Use matrix factorization optimized for Bayesian Personalized Ranking (BPR) to solve the item ranking problem. BPR reduces ranking (in this case: binary variables) to pairwise classiﬁcation. Extend BPR to use diﬀerent sampling scheme: Weighted BPR (WBPR). Open question: Learn a diﬀerent contrast? Details can be found in the paper. Code: http://ismll.de/mymedialite/ examples/kddcup2011.html advertisement: Contribute to http://recsyswiki.com!Zeno Gantner et al., University of Hildesheim 13 / 15
• 15. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items QuestionsZeno Gantner et al., University of Hildesheim 14 / 15
• 16. Bayesian Personalized Rankingfor Non-Uniformly Sampled ItemsAcknowledgements Thank you The organizers, for hosting a great competition. The participants, for sharing their insights. Funding German Research Council (Deutsche Forschungsgemeinschaft, DFG) project Multirelational Factorization Models. Development of the MyMediaLite software was co-funded by the European Commission FP7 project MyMedia under the grant agreement no. 215006. Picture credits by Michael Sauers, under Creative Commons by-nc-sa 2.0 http://www.flickr.com/photos/travelinlibrarian/223839049/ by Rob Starling, under Creative Commons by-sa 2.0 http://en.wikipedia.org/wiki/File:Air_New_Zealand_B747-400_ZK-SUI_at_LHR.jpgZeno Gantner et al., University of Hildesheim 15 / 15
• 17. Bayesian Personalized Rankingfor Non-Uniformly Sampled ItemsNumbers? k error in % “liked” contrast 320 5.52 480 5.08 “rated” contrast 320 5.15 480 4.87 Estimated error on validation split (not leaderboard).Zeno Gantner et al., University of Hildesheim 16 / 15
• 18. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items AdvertisementMyMediaLite: Recommender System Algorithm Library functionality rating prediction item recommendation from implicit feedback group recommendation target groups simple researchers, educators and students free application developers scalable development well-documented written in C#, runs on Mono well-tested GNU General Public License (GPL) choice regular releases (ca. 1 per month) http://ismll.de/mymedialiteZeno Gantner et al., University of Hildesheim 17 / 15
• 19. Bayesian Personalized Rankingfor Non-Uniformly Sampled Items AdvertisementRecSys Wiki is looking for contributions Alan Zeno http://recsyswiki.comZeno Gantner et al., University of Hildesheim 18 / 15