OpenRec: A Modular Framework for Extensible and AdaptableRecommendation Algorithms. 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.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia: Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation (2021) arXiv
- Salah, Aghiles; Truong, Quoc-Tuan; Lauw, Hady W.: Cornac: a comparative framework for multimodal recommender systems (2020)
- Shuai Zhang, Yi Tay, Lina Yao, Bin Wu, Aixin Sun: DeepRec: An Open-source Toolkit for Deep Learning based Recommendation (2019) arXiv