{"type":"ScholarlyArticle","title":"DeepFM: A Factorization-Machine based Neural Network for CTR Prediction","authors":[{"name":"Huifeng Guo","slug":"huifeng-guo","given":"Huifeng","family":"Guo","affiliation":"Shenzhen Graduate School , Harbin Institute of Technology , China; Noah's Ark Research Lab , Huawei."},{"name":"Ruiming TANG","slug":"ruiming-tang","given":"Ruiming","family":"TANG","affiliation":"Noah's Ark Research Lab , Huawei , China"},{"name":"Yunming Ye","slug":"yunming-ye","given":"Yunming","family":"Ye","affiliation":"Shenzhen Graduate School , Harbin Institute of Technology , China"},{"name":"Zhenguo Li","slug":"zhenguo-li","given":"Zhenguo","family":"Li","affiliation":"Noah's Ark Research Lab , Huawei , China"},{"name":"Xiuqiang He","slug":"xiuqiang-he","given":"Xiuqiang","family":"He","affiliation":"Noah's Ark Research Lab , Huawei , China"}],"abstract":"Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared input to its \"wide\" and \"deep\" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.","doi":"10.24963/ijcai.2017/239","url":"https://ijcai.org/proceedings/2017/239","pdf":"https://ijcai.org/proceedings/2017/0239.pdf","markdown":"https://ijcai.org/proceedings/2017/239.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/239","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":239,"pages":{"first":1725,"last":1731},"keywords":["Machine Learning: Machine Learning","Machine Learning: Neural Networks","Machine Learning: Learning Preferences or Rankings","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Boulanger-Lewandowski et al., 2013] Nicolas Boulanger- Lewandowski, Yoshua Bengio, and Pascal Vincent. 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