OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms
OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms
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DOI：
10.1145/3159652.3159681
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发表时间：
2018-02
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影响因子：
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通讯作者：
Longqi Yang;Eugene Bagdasaryan;Joshua Gruenstein;C. Hsieh;D. Estrin
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作者：
Longqi Yang;Eugene Bagdasaryan;Joshua Gruenstein;C. Hsieh;D. Estrin
随着对用户偏好更深入理解的需求不断增加，推荐系统已经超越了简单的用户项过滤，并且越来越复杂，包括用于分析和融合不同信息的多个组件。不幸的是，现有的框架不足以支持可扩展性和适应性，因此对快速，迭代和系统的实验提出了重大挑战。在这项工作中，我们提出了OpenRec，一个开放的和模块化的Python框架，支持可扩展的和适应性强的推荐系统的研究。每个推荐器被建模为一个计算图，该计算图由通过一组定义良好的接口连接的可重用模块的结构化集合组成。我们提出了OpenRec的架构，并证明OpenRec提供了适应性，模块化和可重用性，同时保持训练效率和推荐准确性。我们的案例研究说明了OpenRec如何支持一个高效的设计过程，以原型和基准的替代方法与可互换的模块，并使新算法的开发和评估。
With the increasing demand for deeper understanding of users» preferences, recommender systems have gone beyond simple user-item filtering and are increasingly sophisticated, comprised of multiple components for analyzing and fusing diverse information. Unfortunately, existing frameworks do not adequately support extensibility and adaptability and consequently pose significant challenges to rapid, iterative, and systematic, experimentation. In this work, we propose OpenRec, an open and modular Python framework that supports extensible and adaptable research in recommender systems. Each recommender is modeled as a computational graph that consists of a structured ensemble of reusable modules connected through a set of well-defined interfaces. We present the architecture of OpenRec and demonstrate that OpenRec provides adaptability, modularity and reusability while maintaining training efficiency and recommendation accuracy. Our case study illustrates how OpenRec can support an efficient design process to prototype and benchmark alternative approaches with inter-changeable modules and enable development and evaluation of new algorithms.