• Publications
  • Influence
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. Expand
Explaining the user experience of recommender systems
TLDR
This paper proposes a framework that takes a user-centric approach to recommender system evaluation that links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively). Expand
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. Expand
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. Expand
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. Expand
GQR – A Fast Reasoner for Binary Qualitative Constraint Calculi
GQR (Generic Qualitative Reasoner) is a solver for binary qualitative constraint networks. GQR takes a calculus description and one or more constraint networks as input, and tries to solve theExpand
Factorization models for context-/time-aware movie recommendations
TLDR
An extended version of PITF is presented that handles the week context in a smoother way and is compared against different item recommendation baselines that do not take context into account, and a non-personalized context-aware baseline. Expand
Cost-sensitive learning methods for imbalanced data
TLDR
Two empirical methods that deal with class imbalance using both resampling and CSL are presented, one of which can reduce the misclassification costs, and the second can improve the classifier performance. Expand
Personalized Ranking for Non-Uniformly Sampled Items
TLDR
An adapted version of the Bayesian Personalized Ranking (BPR) optimization criterion that takes the non-uniform sampling of negative test items — as in track 2 of the KDD Cup 2011 — into account is developed and used to train ranking matrix factorization models as components of an ensemble. Expand
Multilingual Lexical Semantic Resources for Ontology Translation
TLDR
This work describes the integration of some multilingual language resources in ontological descriptions, supporting both the semantic annotation of textual documents with multilingual ontology labels and ontology extraction from multilingual text sources. Expand
...
1
2
3
...