activity
20122019
most citedBPR: Bayesian Personalized Ranking from Implicit Feedback

4.4k citations · 4.4k across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG20191 cited

HIDRA: Head Initialization across Dynamic targets for Robust Architectures

Rafael Rego Drumond, Lukas Brinkmeyer, Josif Grabocka +1

The performance of gradient-based optimization strategies depends heavily on the initial weights of the parametric model. Recent works show that there exist weight initializations…

cs.LG2019

Multi-Label Network Classification via Weighted Personalized Factorizations

Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme

Multi-label network classification is a well-known task that is being used in a wide variety of web-based and non-web-based domains. It can be formalized as a multi-relational lear…

cs.CV20194 cited

Data-Driven Vehicle Trajectory Forecasting

Shayan Jawed, Eya Boumaiza, Josif Grabocka +1

An active area of research is to increase the safety of self-driving vehicles. Although safety cannot be guarenteed completely, the capability of a vehicle to predict the future tr…

cs.LG201811 cited

NeuralWarp: Time-Series Similarity with Warping Networks

Josif Grabocka, Lars Schmidt-Thieme

Research on time-series similarity measures has emphasized the need for elastic methods which align the indices of pairs of time series and a plethora of non-parametric have been p…

cs.LG20162 cited

Bank Card Usage Prediction Exploiting Geolocation Information

Martin Wistuba, Nghia Duong-Trung, Nicolas Schilling +1

We describe the solution of team ISMLL for the ECML-PKDD 2016 Discovery Challenge on Bank Card Usage for both tasks. Our solution is based on three pillars. Gradient boosted decisi…

cs.IR20124.4k cited

BPR: Bayesian Personalized Ranking from Implicit Feedback

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner +1

Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario wit…