{"type":"ScholarlyArticle","title":"DeepRec: An Open-source Toolkit for Deep Learning based Recommendation","authors":[{"name":"Shuai Zhang","slug":"shuai-zhang","given":"Shuai","family":"Zhang","affiliation":"University of New South Wales"},{"name":"Yi Tay","slug":"yi-tay","given":"Yi","family":"Tay","affiliation":"Nanyang Technological University"},{"name":"Lina Yao","slug":"lina-yao","given":"Lina","family":"Yao","affiliation":"University of New South Wales"},{"name":"Bin Wu","slug":"bin-wu","given":"Bin","family":"Wu","affiliation":"Zhengzhou Univeristy"},{"name":"Aixin Sun","slug":"aixin-sun","given":"Aixin","family":"Sun","affiliation":"Nanyang Technological University"}],"abstract":"Deep learning based recommender systems have been extensively explored in recent years. However, the large number of models proposed each year poses a big challenge for both researchers and practitioners in reproducing the results for further comparisons. Although a portion of papers provides source code, they adopted different programming languages or different deep learning packages, which also raises the bar in grasping the ideas. To alleviate this problem, we released the open source project: \\textbf{DeepRec}. In this toolkit, we have implemented a number of deep learning based recommendation algorithms using Python and the widely used deep learning package - Tensorflow. Three major recommendation scenarios: rating prediction, top-N recommendation (item ranking) and sequential recommendation, were considered. Meanwhile, DeepRec maintains good modularity and extensibility to easily incorporate new models into the framework. It is distributed under the terms of the GNU General Public License. The source code is available at github: https://github.com/cheungdaven/DeepRec","doi":"10.24963/ijcai.2019/963","url":"https://ijcai.org/proceedings/2019/963","pdf":"https://ijcai.org/proceedings/2019/0963.pdf","markdown":"https://ijcai.org/proceedings/2019/963.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/963","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Demos","track":"Demos","number":963,"pages":{"first":6581,"last":6583},"keywords":["AI: Recommender Systems","AI: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Cheng et al., 2016] Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al. 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