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Deep Matrix Factorization With Implicit Feedback Embedding for Recommendation System

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成果类型:
期刊论文
作者:
Yi, Baolin;Shen, Xiaoxuan;Liu, Hai*;Zhang, Zhaoli;Zhang, Wei;...
通讯作者:
Liu, Hai
作者机构:
[Shen, Xiaoxuan; Liu, Sannyuya; Zhang, Wei; Zhang, Zhaoli; Yi, Baolin; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
[Liu, Sannyuya; Xiong, Naixue] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Hubei, Peoples R China.
通讯机构:
[Liu, Hai] C
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
语种:
英文
关键词:
Collaborative filtering (CF);deep learning (DL);matrix factorization (MF);recommendation system;representation learning
期刊:
IEEE Transactions on Industrial Informatics
ISSN:
1551-3203
年:
2019
卷:
15
期:
8
页码:
4591-4601
基金类别:
National Key R&D Program of China [2017YFB1401300, 2017YFB1401303]; Self-determined Research Funds of CCNU [CCNU18ZDPY10]; Cultivating Excellent Doctoral Dissertations Program of CCNU [2018YBZZ006, TII-18-2022]
机构署名:
本校为第一且通讯机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this paper, we propose a deep learning (DL) based collaborative filtering framework, namely, deep matrix factorization (DMF), which can integrate any kind of side information effectively and handily. In DMF, two feature transforming functions are built to directly generate latent factors of users and items from various input information. As for the implicit feedback that is commonly use...

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