基于民俗学的推荐系统的评估框架

R. García, Matthias Bender, Mojisola Erdt, Christoph Rensing, R. Steinmetz
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引用次数: 5

摘要

FReSET是一个新的推荐系统评估框架,旨在支持基于民俗学的推荐系统的研究。它为实现基于民俗学的推荐系统提供接口,并支持对历史数据进行一致和可重复的离线评估。与其他推荐系统框架项目不同,这里的重点是提供一个灵活的框架,允许用户实现他们自己的基于大众分类法的推荐算法和预处理过滤方法,而不仅仅是提供一组协作过滤实现。FReSET包括一个用于结果可视化的图形界面和不同的交叉验证实现,以补充基本功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FReSET: an evaluation framework for folksonomy-based recommender systems
FReSET is a new recommender systems evaluation framework aiming to support research on folksonomy-based recommender systems. It provides interfaces for the implementation of folksonomy-based recommender systems and supports the consistent and reproducible offline evaluations on historical data. Unlike other recommender systems framework projects, the emphasis here is on providing a flexible framework allowing users to implement their own folksonomy-based recommender algorithms and pre-processing filtering methods rather than just providing a collection of collaborative filtering implementations. FReSET includes a graphical interface for result visualization and different cross-validation implementations to complement the basic functionality.
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