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Steffen Rendle
Researcher at Google
Publications - 68
Citations - 14841
Steffen Rendle is an academic researcher from Google. The author has contributed to research in topics: Recommender system & Ranking (information retrieval). The author has an hindex of 28, co-authored 66 publications receiving 11314 citations. Previous affiliations of Steffen Rendle include University of Konstanz & University of Hildesheim.
Papers
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Proceedings Article
BPR: Bayesian personalized ranking from implicit feedback
TL;DR: In this article, the authors proposed a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem, which is based on stochastic gradient descent with bootstrap sampling.
Proceedings ArticleDOI
Factorization Machines
TL;DR: Factorization Machines (FM) are introduced which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models and can mimic these models just by specifying the input data (i.e. the feature vectors).
Proceedings ArticleDOI
Factorizing personalized Markov chains for next-basket recommendation
TL;DR: This paper introduces an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data and shows that the FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization.
Journal ArticleDOI
Factorization Machines with libFM
TL;DR: The libFM as mentioned in this paper tool is a software implementation for factorization machines that features stochastic gradient descent (SGD) and alternating least-squares (ALS) optimization, as well as Bayesian inference using Markov Chain Monto Carlo (MCMC).
Posted Content
BPR: Bayesian Personalized Ranking from Implicit Feedback
TL;DR: This paper presents a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem and provides a generic learning algorithm for optimizing models with respect to B PR-Opt.