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arXiv · 2508.13019

Informfully Recommenders -- Reproducibility Framework for Diversity-aware Intra-session Recommendations

Lucien Heitz·Runze Li·Oana Inel·Abraham Bernstein
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Abstract

Norm-aware recommender systems have gained increased attention, especially for diversity optimization. The recommender systems community has well-established experimentation pipelines that support reproducible evaluations by facilitating models' benchmarking and comparisons against state-of-the-art methods. However, to the best of our knowledge, there is currently no reproducibility framework to support thorough norm-driven experimentation at the pre-processing, in-processing, post-processing, and evaluation stages of the recommender pipeline. To address this gap, we present Informfully Recommenders, a first step towards a normative reproducibility framework that focuses on diversity-aware design built on Cornac. Our extension provides an end-to-end solution for implementing and experimenting with normative and general-purpose diverse recommender systems that cover 1) dataset pre-processing, 2) diversity-optimized models, 3) dedicated intrasession item re-ranking, and 4) an extensive set of diversity metrics. We demonstrate the capabilities of our extension through an extensive offline experiment in the news domain.

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Lucien Heitz, Runze Li, Oana Inel, Abraham Bernstein. 2025-08-18. Informfully Recommenders -- Reproducibility Framework for Diversity-aware Intra-session Recommendations. https://doi.org/10.1145/3705328.3748148

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