68 citations · 250 across the 51 of their papers we have counts for
5 papers · 1 filter
Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks
Tomislav Duricic, Dominik Kowald, Emanuel Lacic +1
By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches…
Reproducibility in Machine Learning-Driven Research
Harald Semmelrock, Simone Kopeinik, Dieter Theiler +2
Research is facing a reproducibility crisis, in which the results and findings of many studies are difficult or even impossible to reproduce. This is also the case in machine learn…
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations
Dominik Kowald, Gregor Mayr, Markus Schedl +1
Recent research has suggested different metrics to measure the inconsistency of recommendation performance, including the accuracy difference between user groups, miscalibration, a…
A conceptual model for leaving the data-centric approach in machine learning
Sebastian Scher, Bernhard Geiger, Simone Kopeinik +2
For a long time, machine learning (ML) has been seen as the abstract problem of learning relationships from data independent of the surrounding settings. This has recently been cha…
Uptrendz: API-Centric Real-time Recommendations in Multi-Domain Settings
Emanuel Lacic, Tomislav Duricic, Leon Fadljevic +2
In this work, we tackle the problem of adapting a real-time recommender system to multiple application domains, and their underlying data models and customization requirements. To…