标题:Discrete factorization machines for fast feature-based recommendation
作者:Liu, Han ;He, Xiangnan ;Feng, Fuli ;Nie, Liqiang ;Liu, Rui ;Zhang, Hanwang
通讯作者:Nie, Liqiang
作者机构:[Liu, Han ;Nie, Liqiang ] School of Computer Science and Technology, Shandong University, United Kingdom;[Liu, Rui ] University of Electronic Science 更多
会议名称:27th International Joint Conference on Artificial Intelligence, IJCAI 2018
会议日期:13 July 2018 through 19 July 2018
来源:IJCAI International Joint Conference on Artificial Intelligence
出版年:2018
卷:2018-July
页码:3449-3455
摘要:User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 107, results in expensive storage and computational cost. This prohibits fast recommendation especially on mobile applications where the computational resource is very limited. In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation. DFM binarizes the real-valued model parameters (e.g., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports efficient storage and fast user-item score computation. To avoid the severe quantization loss of the binarization, we propose a convergent updating rule that resolves the challenging discrete optimization of DFM. Through extensive experiments on two real-world datasets, we show that 1) DFM consistently outperforms state-of-the-art binarized recommendation models, and 2) DFM shows very competitive performance compared to its real-valued version (FM), demonstrating the minimized quantization loss. © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055704848&partnerID=40&md5=4071af53896e617f042b68fb04363b1e
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