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19982026
most citedLearning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle

200 citations

Showing 2019Show all

7 papers · 1 filter

cs.LG2019★ 2 cited

Robust Parameter-Free Season Length Detection in Time Series

Maximilian Toller, Roman Kern

The in-depth analysis of time series has gained a lot of research interest in recent years, with the identification of periodic patterns being one important aspect. Many of the met…

cs.DB2019★ 18 cited

SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle

Matthias Boehm, Iulian Antonov, Sebastian Baunsgaard +10

Machine learning (ML) applications become increasingly common in many domains. ML systems to execute these workloads include numerical computing frameworks and libraries, ML algori…

cs.IR2019

Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets

Dominik Kowald, Matthias Traub, Dieter Theiler +5

This work addresses the problem of providing and evaluating recommendations in data markets. Since most of the research in recommender systems is focused on the bipartite relations…

cs.IR2019★ 1 cited

Modeling Artist Preferences of Users with Different Music Consumption Patterns for Fair Music Recommendations

Dominik Kowald, Elisabeth Lex, Markus Schedl

Music recommender systems have become central parts of popular streaming platforms such as Last.fm, Pandora, or Spotify to help users find music that fits their preferences. These…

cs.IR2019

Should we Embed? A Study on the Online Performance of Utilizing Embeddings for Real-Time Job Recommendations

Markus Reiter-Haas, Emanuel Lacic, Tomislav Duricic +2

In this work, we present the findings of an online study, where we explore the impact of utilizing embeddings to recommend job postings under real-time constraints. On the Austrian…

cs.SI2019

Exploiting weak ties in trust-based recommender systems using regular equivalence

Tomislav Duricic, Emanuel Lacic, Dominik Kowald +1

User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. CF, however, suffers from data sparsity and the cold-start problem sinc…