首个面向多样性推荐的可复现框架，支持全流程实验
Informfully Recommenders -- Reproducibility Framework for Diversity-aware Intra-session Recommendations
- 基于Cornac构建全流程多样性推荐框架
- 涵盖数据预处理到评分重排的完整链条
- 适合研究推荐系统多样性的学者与工程师
规范感知推荐系统在多样性优化方面日益受到关注。虽然推荐系统社区已建立成熟的实验流程以支持可复现评估，便于模型基准测试与先进方法对比，但目前尚无针对推荐管道中预处理、过程处理、后处理和评估阶段的规范驱动实验的可复现框架。为填补这一空白，我们提出Informfully Recommenders——首个聚焦于多样性感知设计的规范可复现框架，基于Cornac实现。该扩展提供端到端解决方案，支持规范性和通用多样性推荐系统的实现与实验，涵盖：1）数据集预处理；2）多样性优化模型；3）专用会话内项目重排序；4）一套全面的多样性度量指标。我们通过新闻领域的大量离线实验展示了该框架的能力。
原文摘要 · Abstract (English)
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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