Longqi Yang, Eugene Bagdasaryan, Joshua Gruenstein, Cheng-Kang Hsieh, Deborah Estrin
2018 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM '18  
With the increasing demand for deeper understanding of users' preferences, recommender systems have gone beyond simple user-item ltering 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 signi cant challenges to rapid, iterative, and systematic, experimentation. In this work, we propose OpenRec, an open and modular
more » ... on 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-de ned interfaces. We present the architecture of OpenRec and demonstrate that OpenRec provides adaptability, modularity and reusability while maintaining training e ciency and recommendation accuracy. Our case study illustrates how OpenRec can support an e cient design process to prototype and benchmark alternative approaches with inter-changeable modules and enable development and evaluation of new algorithms.
doi:10.1145/3159652.3159681 dblp:conf/wsdm/YangBGHE18 fatcat:ev43dr4bp5emnopg4gnrxjszua