librec-auto: A Tool for Recommender Systems Experimentation
librec-auto: A Tool for Recommender Systems Experimentation
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DOI：
10.1145/3459637.3482006
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Nasim Sonboli;M. Mansoury;Ziyue Guo;Shreyas Kadekodi;Weiwen Liu;Zijun Liu;Andrew Schwartz;R. Burke
中科院分区：
文献类型：
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作者：
Nasim Sonboli;M. Mansoury;Ziyue Guo;Shreyas Kadekodi;Weiwen Liu;Zijun Liu;Andrew Schwartz;R. Burke
推荐系统很复杂。它们将用户的个人需求与特定应用领域的特征相结合，这些应用领域可能涵盖来自大型且可能具有异构性的集合中的项目。需要进行大量实验来理解推荐算法的多维属性以及算法与应用之间的契合度。Librec - auto是一种工具，它能自动完成离线批量推荐系统实验的许多方面。它拥有一个包含先进的和历史上的推荐算法的大型库以及各种各样的评估指标。它还通过集成重排序算法和具有公平性意识的指标来支持对推荐中的多样性和公平性的研究。它支持用于可重复性实验管理的声明式配置，并支持多种形式的超参数优化。
Recommender systems are complex. They integrate the individual needs of users with the characteristics of particular domains of application which may span items from large and potentially heterogeneous collections. Extensive experimentation is required to understand the multidimensional properties of recommendation algorithms and the fit between algorithm and application. librec-auto is a tool that automates many aspects of off-line batch recommender system experimentation. It has a large library of state-of-the-art and historical recommendation algorithms and a wide variety of evaluation metrics. It further supports the study of diversity and fairness in recommendation through the integration of re-ranking algorithms and fairness-aware metrics. It supports declarative configuration for reproducible experiment management and supports multiple forms of hyper-parameter optimization.