用于个性化推荐的条件卷积隐因子模型

doi:10.19678/j.issn.1000-3428.0054209

计算机工程 ›› 2020, Vol. 46 ›› Issue (4): 85-90,96. doi: 10.19678/j.issn.1000-3428.0054209

用于个性化推荐的条件卷积隐因子模型

李南星^1,2, 盛益强^1,2, 倪宏^1,2

1. 中国科学院声学研究所国家网络新媒体工程技术研究中心, 北京 100190;
2. 中国科学院大学电子电气与通信工程学院, 北京 100049

收稿日期:2019-03-13 修回日期:2019-04-15 出版日期:2020-04-15 发布日期:2019-05-16
作者简介:李南星(1991-),男,博士研究生,主研方向为人工智能、深度学习、数据挖掘;盛益强,博士;倪宏,研究员。
基金资助:
中国科学院先导专项课题"SEANET技术标准化研究与系统研制"（XDC02010801）。

Conditional Convolution Implicit Factor Model for Personalized Recommendation

LI Nanxing^1,2, SHENG Yiqiang^1,2, NI Hong^1,2

1. National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China;
2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2019-03-13 Revised:2019-04-15 Online:2020-04-15 Published:2019-05-16

摘要/Abstract

摘要： 在推荐系统中，传统的矩阵分解无法提取用户和物品特征，而神经协同过滤（NCF）在分解模型中增加多层感知器，但不能有效利用用户和物品ID之外的辅助信息。为此，提出一种新的条件卷积方法。通过将物品特征作为输入，将用户特征作为卷积核，达到权值不共享的目的，使得条件卷积具有更强的特征提取和组合能力以及不增加参数量的特性。在此基础上，条件卷积能够融入多种辅助信息进行个性化推荐。实验结果表明，与NCF模型相比，该方法在隐性反馈数据中推荐命中率提升3.11%，在显性反馈数据中评分预测误差降低2.47%。

Abstract: In the recommendation system,the traditional Matrix Factorization(MF) cannot extract the features of users and items.The Neural Collaborative Filtering(NCF) adds a multi-layer perceptron to the model for this reason,but still cannot effectively make use of the auxiliary information other than the user and item ID.Therefore,this paper proposes a new conditional convolution method.In this method,the item features are used as input and the user features as convolutional kernels,so as to achieve the purpose of unshared weights,thus making the conditional convolution has stronger feature extraction and combination abilities while maintaining the features of parameters.On this basis,the conditional convolution can incorporate a variety of auxiliary information for personalized recommendation.Experimental results show that compared with the NCF model,the proposed method can increase the recommendation hit rate by 3.11% in the implicit feedback data and reduce the scoring prediction error by 2.47% in the explicit feedback data.

Key words: recommendation system, Deep Neural Networks(DNN), Neural Collaborative Filtering(NCF), conditional convolution, Matrix Factorization(MF)

中图分类号:

TP18

李南星, 盛益强, 倪宏. 用于个性化推荐的条件卷积隐因子模型[J]. 计算机工程, 2020, 46(4): 85-90,96.

LI Nanxing, SHENG Yiqiang, NI Hong. Conditional Convolution Implicit Factor Model for Personalized Recommendation[J]. Computer Engineering, 2020, 46(4): 85-90,96.

https://www.ecice06.com/CN/Y2020/V46/I4/85

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参考文献 22

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