L. Schmidt-Thieme

Publications298
h-index44
Citations13,995
Highly Influential Citations2,033
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BPR: Bayesian Personalized Ranking from Implicit Feedback
TLDR
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.
View PDF on arXiv
Factorizing personalized Markov chains for next-basket recommendation
TLDR
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.
View on ACM
Pairwise interaction tensor factorization for personalized tag recommendation
TLDR
The factorization model PITF (Pairwise Interaction Tensor Factorization) is presented which is a special case of the TD model with linear runtime both for learning and prediction and shows that this model outperforms TD largely in runtime and even can achieve better prediction quality.
View on ACM
Tag Recommendations in Folksonomies
TLDR
This paper evaluates and compares two recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank, showing that both provide better results than non-personalized baseline methods.
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Learning time-series shapelets
TLDR
A new mathematical formalization of the task via a classification objective function is proposed and a tailored stochastic gradient learning algorithm is applied and can learn true top-K shapelets by capturing their interaction.
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Learning optimal ranking with tensor factorization for tag recommendation
TLDR
This paper proposes a method for tag recommendation based on tensor factorization (TF) and provides a gradient descent algorithm to solve the optimization problem and demonstrates that this method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.
View on ACM
Fast context-aware recommendations with factorization machines
TLDR
This work proposes to apply Factorization Machines (FMs) to model contextual information and to provide context-aware rating predictions and shows empirically that this approach outperforms Multiverse Recommendation in prediction quality and runtime.
View on ACM
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations
TLDR
This work uses the mapping concept to construct an attribute-aware matrix factorization model for item recommendation from implicit, positive-only feedback, and shows that this approach provides good predictive accuracy, while the prediction time only grows by a constant factor.
View on IEEE
MyMediaLite: a free recommender system library
TLDR
The library addresses two common scenarios in collaborative filtering: rating prediction and item prediction from positive-only implicit feedback, and contains methods for real-time updates and loading/storing of already trained recommender models.
View on ACM
Tag-aware recommender systems by fusion of collaborative filtering algorithms
TLDR
A generic method is proposed that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three two- dimensional correlations and then applying a fusion method to re-associate these correlations.
View on ACM
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