arXiv AI By Nassim Belmecheri, Noureddine Aribi, Nadjib Lazaar, Yahia Lebbah, Samir Loudni

Boosting the Learning for Ranking Patterns

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arXiv AI
Sep 28

SPADE: Escaping the Popularity-Similarity Frontier to Measure Serendipitous Recommendations

SPADE (Serendipitous Pareto Distance Evaluation) is a new metric for recommender systems that simultaneously considers item similarity, popularity, and user relevance. It projects items into a two‑dimensional space and computes a user‑specific Pareto frontier of maximally popular and historically similar items, then averages the minimum Euclidean distance from this frontier for correctly recommended test‑set items. Experiments on five datasets and five baseline algorithms demonstrate that SPADE effectively discourages algorithms from exploiting accuracy‑only metrics and reliably isolates serendipitous discoveries.

By Tobias Vente, Maarten Peirsman, Noah Dani\"els, Hannu Toivonen, Bart Goethals
arXiv Machine Learning
Jun 8

Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies

arXiv:2606. 07492v1 Announce Type: cross Abstract: The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale.

By Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov, Ilya Ivanov, Daria Korovaitceva, Margarita Rusanova, Uliana Parkina, Alexander Derevyagin, Evgeny Frolov, Sergey Samsonov, Anton Lysenko