{"type":"ScholarlyArticle","title":"DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation","authors":[{"name":"Weiyu Cheng","slug":"weiyu-cheng","given":"Weiyu","family":"Cheng","affiliation":"Department of Computer Science and Engineering Shanghai Jiao Tong University"},{"name":"Yanyan Shen","slug":"yanyan-shen","given":"Yanyan","family":"Shen","affiliation":"Department of Computer Science and Engineering Shanghai Jiao Tong University"},{"name":"Yanmin Zhu","slug":"yanmin-zhu","given":"Yanmin","family":"Zhu","affiliation":"Department of Computer Science and Engineering Shanghai Jiao Tong University"},{"name":"Linpeng Huang","slug":"linpeng-huang","given":"Linpeng","family":"Huang","affiliation":"Department of Computer Science and Engineering Shanghai Jiao Tong University"}],"abstract":"Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents  users based on their interacted items, this paper proposes a dual-embedding based deep latent factor model named DELF for recommendation with implicit feedback. In addition to learning a single embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users). We employ an attentive neural method to discriminate the importance of interacted users/items for dual-embedding learning. We further introduce a neural network architecture to incorporate dual embeddings for recommendation. A novel attempt of DELF is to model each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of DELF on item recommendation.","doi":"10.24963/ijcai.2018/462","url":"https://ijcai.org/proceedings/2018/462","pdf":"https://ijcai.org/proceedings/2018/0462.pdf","markdown":"https://ijcai.org/proceedings/2018/462.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/462","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":462,"pages":{"first":3329,"last":3335},"keywords":["Machine Learning: Deep Learning","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal and Parthasarathy, 2001] Charu C. 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