《计算机应用》唯一官方网站 ›› 2021, Vol. 41 ›› Issue (12): 3499-3507.DOI: 10.11772/j.issn.1001-9081.2021060894
• 第十八届中国机器学习会议(CCML 2021) • 上一篇
收稿日期:
2021-05-12
修回日期:
2021-06-14
接受日期:
2021-06-23
发布日期:
2021-08-20
出版日期:
2021-12-10
通讯作者:
潘微科
作者简介:
陈宪聪(1995—),男,广东茂名人,硕士研究生,主要研究方向:个性化推荐、迁移学习基金资助:
Xiancong CHEN1,2,3, Weike PAN1,2,3(), Zhong MING1,2,3
Received:
2021-05-12
Revised:
2021-06-14
Accepted:
2021-06-23
Online:
2021-08-20
Published:
2021-12-10
Contact:
Weike PAN
About author:
CHEN Xiancong, born in 1995, M. S. candidate. His research interests include personalized recommendation, transfer learning.Supported by:
摘要:
在推荐系统领域,大部分现有的工作主要关注仅有一种类型的用户反馈(如购买反馈)的单类协同过滤(OCCF)问题。然而,在现实的应用中,用户的反馈往往是异构的,因此如何对用户的异构反馈进行建模从而准确刻画用户的真实偏好成为了一个新的挑战。围绕异构单类协同过滤(HOCCF)问题(包含了用户的购买反馈和浏览反馈),提出了一个迁移学习解决方案——阶段式变分自编码器(SVAE)模型。首先,将用户的浏览反馈当作辅助数据,以多项式变分自编码器(Multi-VAE)为基础模型学习并生成隐特征向量;然后迁移该隐特征向量到另一路Multi-VAE,用于帮助该Multi-VAE对用户的目标数据(即购买反馈)进行建模。三个真实数据集上的实验结果显示,在多数情况下,SVAE模型在精确度(Precision@5)、归一化折损累计增益(NDCG@5)等重要指标上的表现显著优于其他流行的推荐算法,验证了所提模型的有效性。
中图分类号:
陈宪聪, 潘微科, 明仲. 面向异构单类协同过滤的阶段式变分自编码器[J]. 计算机应用, 2021, 41(12): 3499-3507.
Xiancong CHEN, Weike PAN, Zhong MING. Staged variational autoencoder for heterogeneous one-class collaborative filtering[J]. Journal of Computer Applications, 2021, 41(12): 3499-3507.
符号 | 含义 |
---|---|
用户数 | |
物品数 | |
用户ID | |
物品ID | |
用户集合 | |
物品集合 | |
所有浏览记录集合 | |
所有购买记录集合 | |
用户 | |
浏览模型编码器生成的隐向量 | |
用户 | |
购买模型编码器生成的隐向量 | |
浏览模型变分自编码器参数 | |
购买模型变分自编码器参数 | |
迭代次数 | |
学习率 | |
正则化参数 |
表1 符号说明
Tab. 1 Symbol description
符号 | 含义 |
---|---|
用户数 | |
物品数 | |
用户ID | |
物品ID | |
用户集合 | |
物品集合 | |
所有浏览记录集合 | |
所有购买记录集合 | |
用户 | |
浏览模型编码器生成的隐向量 | |
用户 | |
购买模型编码器生成的隐向量 | |
浏览模型变分自编码器参数 | |
购买模型变分自编码器参数 | |
迭代次数 | |
学习率 | |
正则化参数 |
数据集 | 用户数 | 物品数 | 购买记录数 | 浏览记录数 |
---|---|---|---|---|
ML10M | 71 567 | 10 681 | 309 317 | 4 000 024 |
Netflix | 480 189 | 17 770 | 4 554 888 | 39 628 846 |
Rec15 | 36 917 | 9 621 | 159 429 | 213 332 |
表2 处理后的三个数据集的统计信息
Tab. 2 Statistics of three datasets after processing
数据集 | 用户数 | 物品数 | 购买记录数 | 浏览记录数 |
---|---|---|---|---|
ML10M | 71 567 | 10 681 | 309 317 | 4 000 024 |
Netflix | 480 189 | 17 770 | 4 554 888 | 39 628 846 |
Rec15 | 36 917 | 9 621 | 159 429 | 213 332 |
数据集 | 方法 | Precision@5 | Recall@5 | F1@5 | NDCG@5 | 1-call@5 |
---|---|---|---|---|---|---|
ML10M | BPR | 0.068 0±0.000 2 | 0.091 5±0.000 3 | 0.065 4±0.000 1 | 0.093 3±0.000 4 | 0.283 7±0.002 3 |
MFLogLoss | 0.073 6±0.000 5 | 0.099 5±0.001 0 | 0.070 5±0.000 7 | 0.101 9±0.000 4 | 0.303 4±0.001 7 | |
Multi-VAE | 0.074 4±0.000 6 | 0.099 6±0.000 9 | 0.070 9±0.000 5 | 0.102 9±0.000 8 | 0.306 2±0.002 3 | |
RoToR | 0.087 2±0.000 1 | 0.123 9±0.000 7 | 0.085 7±0.000 1 | 0.123 5±0.000 6 | 0.356 2±0.000 8 | |
SVAE(B+P) | 0.080 4±0.000 5 | 0.114 6±0.000 1 | 0.079 1±0.000 4 | 0.112 6±0.000 6 | 0.331 8±0.001 3 | |
SVAE(B) | 0.093 2±0.000 5 | 0.137 3±0.000 9 | 0.093 6±0.000 5 | 0.137 1±0.001 0 | 0.374 9±0.002 6 | |
Netflix | BPR | 0.075 5±0.000 4 | 0.050 3±0.000 5 | 0.048 1±0.000 3 | 0.085 4±0.000 4 | 0.299 4±0.001 3 |
MFLogLoss | 0.078 5±0.000 3 | 0.054 9±0.000 6 | 0.051 4±0.000 4 | 0.090 0±0.000 4 | 0.310 3±0.001 4 | |
Multi-VAE | 0.085 8±0.000 3 | 0.059 2±0.000 2 | 0.055 1±0.000 2 | 0.099 4±0.000 2 | 0.331 5±0.001 2 | |
RoToR | 0.094 1±0.000 3 | 0.075 0±0.000 3 | 0.064 7±0.000 3 | 0.111 9±0.000 4 | 0.367 4±0.001 0 | |
SVAE(B+P) | 0.087 7±0.000 3 | 0.068 3±0.000 6 | 0.059 5±0.000 4 | 0.102 9±0.000 4 | 0.345 8±0.001 6 | |
SVAE(B) | 0.098 2±0.000 6 | 0.079 3±0.000 6 | 0.068 5±0.000 5 | 0.118 3±0.000 7 | 0.378 7±0.001 6 | |
Rec15 | BPR | 0.045 7 | 0.228 6 | 0.076 2 | 0.147 3 | 0.228 6 |
MFLogLoss | 0.049 0 | 0.245 1 | 0.081 7 | 0.158 6 | 0.245 1 | |
Multi-VAE | 0.051 0 | 0.255 2 | 0.085 1 | 0.167 0 | 0.255 2 | |
RoToR | 0.053 4 | 0.266 9 | 0.089 0 | 0.173 4 | 0.266 9 | |
SVAE(B+P) | 0.049 7 | 0.248 5 | 0.082 8 | 0.157 0 | 0.248 5 | |
SVAE(B) | 0.053 3 | 0.266 7 | 0.088 9 | 0.177 1 | 0.266 7 |
表3 各模型在三个数据集上的实验结果
Tab. 3 Experimental results of each model on three datasets
数据集 | 方法 | Precision@5 | Recall@5 | F1@5 | NDCG@5 | 1-call@5 |
---|---|---|---|---|---|---|
ML10M | BPR | 0.068 0±0.000 2 | 0.091 5±0.000 3 | 0.065 4±0.000 1 | 0.093 3±0.000 4 | 0.283 7±0.002 3 |
MFLogLoss | 0.073 6±0.000 5 | 0.099 5±0.001 0 | 0.070 5±0.000 7 | 0.101 9±0.000 4 | 0.303 4±0.001 7 | |
Multi-VAE | 0.074 4±0.000 6 | 0.099 6±0.000 9 | 0.070 9±0.000 5 | 0.102 9±0.000 8 | 0.306 2±0.002 3 | |
RoToR | 0.087 2±0.000 1 | 0.123 9±0.000 7 | 0.085 7±0.000 1 | 0.123 5±0.000 6 | 0.356 2±0.000 8 | |
SVAE(B+P) | 0.080 4±0.000 5 | 0.114 6±0.000 1 | 0.079 1±0.000 4 | 0.112 6±0.000 6 | 0.331 8±0.001 3 | |
SVAE(B) | 0.093 2±0.000 5 | 0.137 3±0.000 9 | 0.093 6±0.000 5 | 0.137 1±0.001 0 | 0.374 9±0.002 6 | |
Netflix | BPR | 0.075 5±0.000 4 | 0.050 3±0.000 5 | 0.048 1±0.000 3 | 0.085 4±0.000 4 | 0.299 4±0.001 3 |
MFLogLoss | 0.078 5±0.000 3 | 0.054 9±0.000 6 | 0.051 4±0.000 4 | 0.090 0±0.000 4 | 0.310 3±0.001 4 | |
Multi-VAE | 0.085 8±0.000 3 | 0.059 2±0.000 2 | 0.055 1±0.000 2 | 0.099 4±0.000 2 | 0.331 5±0.001 2 | |
RoToR | 0.094 1±0.000 3 | 0.075 0±0.000 3 | 0.064 7±0.000 3 | 0.111 9±0.000 4 | 0.367 4±0.001 0 | |
SVAE(B+P) | 0.087 7±0.000 3 | 0.068 3±0.000 6 | 0.059 5±0.000 4 | 0.102 9±0.000 4 | 0.345 8±0.001 6 | |
SVAE(B) | 0.098 2±0.000 6 | 0.079 3±0.000 6 | 0.068 5±0.000 5 | 0.118 3±0.000 7 | 0.378 7±0.001 6 | |
Rec15 | BPR | 0.045 7 | 0.228 6 | 0.076 2 | 0.147 3 | 0.228 6 |
MFLogLoss | 0.049 0 | 0.245 1 | 0.081 7 | 0.158 6 | 0.245 1 | |
Multi-VAE | 0.051 0 | 0.255 2 | 0.085 1 | 0.167 0 | 0.255 2 | |
RoToR | 0.053 4 | 0.266 9 | 0.089 0 | 0.173 4 | 0.266 9 | |
SVAE(B+P) | 0.049 7 | 0.248 5 | 0.082 8 | 0.157 0 | 0.248 5 | |
SVAE(B) | 0.053 3 | 0.266 7 | 0.088 9 | 0.177 1 | 0.266 7 |
数据集 | 购买记录数∶ 浏览记录数 | 平均物品数 | 稀疏度/% | 最优模型 |
---|---|---|---|---|
ML10M | 1∶13.93 | 60.21 | 0.56 | SVAE(B) |
Netflix | 1∶8.70 | 92.01 | 0.52 | SVAE(B) |
Rec15 | 1∶1.33 | 10.10 | 0.11 | SVAE(B) |
表4 三个数据集的特点及取得最优推荐效果的模型
Tab. 4 Characteristics of three datasets and model with the optimal recommendation results
数据集 | 购买记录数∶ 浏览记录数 | 平均物品数 | 稀疏度/% | 最优模型 |
---|---|---|---|---|
ML10M | 1∶13.93 | 60.21 | 0.56 | SVAE(B) |
Netflix | 1∶8.70 | 92.01 | 0.52 | SVAE(B) |
Rec15 | 1∶1.33 | 10.10 | 0.11 | SVAE(B) |
数据集 | 方法 | Precision@5 | NDCG@5 |
---|---|---|---|
ML10M | SVAE(alt.) | 0.093 2±0.000 5 | 0.137 1±0.001 0 |
SVAE(seq.) | 0.090 1±0.000 7 | 0.132 5±0.001 0 | |
Netflix | SVAE(alt.) | 0.098 2±0.000 6 | 0.118 3±0.000 7 |
SVAE(seq.) | 0.101 1±0.000 6 | 0.121 9±0.000 7 | |
Rec15 | SVAE(alt.) | 0.053 3 | 0.177 1 |
SVAE(seq.) | 0.053 0 | 0.176 4 |
表5 SVAE(alt.)与SVAE(seq.)在三个数据集上的实验结果
Tab. 5 Experimental results of SVAE(alt.) and SVAE(seq.) on three datasets
数据集 | 方法 | Precision@5 | NDCG@5 |
---|---|---|---|
ML10M | SVAE(alt.) | 0.093 2±0.000 5 | 0.137 1±0.001 0 |
SVAE(seq.) | 0.090 1±0.000 7 | 0.132 5±0.001 0 | |
Netflix | SVAE(alt.) | 0.098 2±0.000 6 | 0.118 3±0.000 7 |
SVAE(seq.) | 0.101 1±0.000 6 | 0.121 9±0.000 7 | |
Rec15 | SVAE(alt.) | 0.053 3 | 0.177 1 |
SVAE(seq.) | 0.053 0 | 0.176 4 |
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