Abstract. Many recommender-system datasets are pruned, i.e. some data is removed that wouldn’t be removed in a production recommender-system. For instance, the MovieLens dataset contains only data from users who rated 20 or more movies.1 Similarly, some researchers prune data themselves and conduct their experiments only on subsets of the Read more…
Update 2019-08-12: Some numbers presented in this post were incorrect, and we updated them. For the latest full analysis, please refer to extended pre-print of Data Pruning in Recommender Systems Research: Best Practice or Malpractice? Datasets are a key ingredient to successful research in the field of recommender systems and Read more…
Our paper “A Novel Approach to Recommendation Algorithm Selection using Meta-Learning” was accepted for publication at the 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS): Introduction The ‘algorithm selection problem’ describes the challenge of finding the most effective algorithm for a given recommendation scenario. Some typical recommendation scenarios are Read more…