Lukas Wegmeth
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View article: Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization
Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization Open
As global warming soars, the need to assess and reduce the environmental impact of recommender systems is becoming increasingly urgent. Despite this, the recommender systems community hardly understands, addresses, and evaluates the enviro…
View article: Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization
Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization Open
As global warming soars, the need to assess and reduce the environmental impact of recommender systems is becoming increasingly urgent. Despite this, the recommender systems community hardly understands, addresses, and evaluates the enviro…
View article: e-Fold Cross-Validation for Recommender-System Evaluation
e-Fold Cross-Validation for Recommender-System Evaluation Open
To combat the rising energy consumption of recommender systems we implement a novel alternative for k-fold cross validation. This alternative, named e-fold cross validation, aims to minimize the number of folds to achieve a reduction in po…
View article: Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets
Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets Open
The recommender systems algorithm selection problem for ranking prediction on\nimplicit feedback datasets is under-explored. Traditional approaches in\nrecommender systems algorithm selection focus predominantly on rating\nprediction on ex…
View article: From Clicks to Carbon: The Environmental Toll of Recommender Systems
From Clicks to Carbon: The Environmental Toll of Recommender Systems Open
As global warming soars, the need to assess the environmental impact of\nresearch is becoming increasingly urgent. Despite this, few recommender systems\nresearch papers address their environmental impact. In this study, we estimate\nthe e…
View article: EMERS: Energy Meter for Recommender Systems
EMERS: Energy Meter for Recommender Systems Open
Due to recent advancements in machine learning, recommender systems use increasingly more energy for training, evaluation, and deployment. However, the recommender systems community often does not report the energy consumption of their exp…
View article: e-fold cross-validation: A computing and energy-efficient alternative to k-fold cross-validation with adaptive folds
e-fold cross-validation: A computing and energy-efficient alternative to k-fold cross-validation with adaptive folds Open
We present the idea of "e-fold" cross-validation. The core idea is that e is chosen ’intelligently’ and individually for each experiment and dataset. This contrasts a static k chosen by gut feeling and past experiences on what k is ’typica…
View article: Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems Open
The hyperparameters of recommender systems for top-n predictions are typically optimized to enhance the predictive performance of algorithms. Thereby, the optimization algorithm, e.g., grid search or random search, searches for the best hy…
View article: The Impact of Feature Quantity on Recommendation Algorithm Performance: A Movielens-100K Case Study
The Impact of Feature Quantity on Recommendation Algorithm Performance: A Movielens-100K Case Study Open
Recent model-based Recommender Systems (RecSys) algorithms emphasize on the use of features, also called side information, in their design similar to algorithms in Machine Learning (ML). In contrast, some of the most popular and traditiona…
View article: A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in Cars
A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in Cars Open
Identifying accident patterns is one of the most vital research foci of driving analysis. Environmental or safety applications and the growing area of fleet management all benefit from accident detection contributions by minimizing the ris…
View article: Detecting Handwritten Mathematical Terms with Sensor Based Data
Detecting Handwritten Mathematical Terms with Sensor Based Data Open
In this work we propose a solution to the UbiComp 2021 Challenge by Stabilo in which handwritten mathematical terms are supposed to be automatically classified based on time series sensor data captured on the DigiPen. The input data set co…