Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets
Lukas Wegmeth, Tobias Vente, Joeran Beel · 2024
The recommender systems algorithm selection problem for ranking prediction on implicit feedback datasets is under-explored. Traditional approaches in recommender systems algorithm selection focus predominantly on rating prediction on explicit feedback datasets, leaving a research gap for ranking prediction on implicit feedback datasets. Algorithm selection is a critical challenge for nearly every practitioner in recommender systems. In this work, we take the first steps toward addressing this research gap.