基于用户多特征融合的个性化推荐模型

doi:10.11959/j.issn.1000-0801.2023111

Abstract

Abstract:

Personalized recommendation is one of the most effective means to extract specific information.Aiming at the problem that lacking of users feature in traditional recommendation methods, a hybrid recommendation model of generalized matrix factorization and deep long short-term memory network based on multi-features fusion (HMF) was proposed.The model used potential eigenvector factors to characterize the selective impact of age and occupation on the project, highlighting user personalization.Long short-term memory (LSTM) was used to obtain the temporal characteristics between users and projects, and then the deep temporal nonlinear higher-order interaction relationship was mined by multi-layer perceptron.The simple nonlinear low-order interaction obtained by generalized matrix factorization (GMF) was fused with the complex time-series nonlinear high-order interaction, and the final prediction score was obtained through the full connection layer.HMF effectively utilized the user’s multi-feature and user-project interaction information to realize personalized dynamic and accurate recommendation.In order to verify the validity and feasibility of the model, the test was conducted on the public dataset MovieLens-1M.The simulation experiment shows that when the potential eigenvector factors of HMF is 50 and the MLP layer is 7, HR@10 and NDCG@10 are 0.983 1 and 0.974 9 respectively, which are 27.61% and 54.29% higher than the optimal solution of the traditional single feature model NCF.

Key words: personalized recommendation, multi-features fusion, deep learning

CLC Number:

TP391.3

Min LU, Juan HU, Xianchao ZHANG, Weijian DING, Guangxue YUE. Personalized recommendation model based on users multi-features fusion[J]. Telecommunications Science, 2023, 39(5): 101-115.

Figures/Tables 12

符号	描述
$U = {u_{1}, u_{2}, \dots, u_{K}}$	用户ID集合
$I = {i_{1}, i_{2}, \dots, i_{W}}$	项目ID集合
$A = {a_{1}, a_{2}, \dots, a_{7}}$	用户年龄集合
$J = {j_{1}, j_{2}, \dots, j_{21}}$	用户职业集合
K	用户ID数目
W	项目ID数目
Y	真实评分
$\tilde{Y}$	预测评分
$m_{u}$	用户ID潜在特征向量
$m_{a}$	用户年龄段潜在特征向量
$m_{j}$	用户职业潜在特征向量
$l_{i}$	项目ID潜在特征向量
$m_{u i} 、 m_{u r}$	LSTM+MLP、GMF模块的用户ID特征向量
$m_{a i} 、 m_{a r}$	LSTM+MLP、GMF模块的用户年龄段特征向量
$m_{j i} 、 m_{j r}$	LSTM+MLP、GMF模块的用户职业特征向量
$l_{i i} 、 l_{i r}$	LSTM+MLP、GMF模块的项目ID特征向量
N	潜在特征向量维度
$U_{K \times N}$	用户ID潜在特征矩阵
$I_{W \times N}$	项目ID潜在特征矩阵
$A_{7 \times N}$	用户年龄段潜在特征矩阵
$J_{21 \times N}$	用户职业潜在特征矩阵

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ID	年龄段
0	＜18
1	18～24
2	25～34
3	35～44
4	45～49
5	50～55
6	＞56

ID	名称
0	other or not specified（其他或未指定）
1	academic/educator（学术/教育工作者）
2	artist（艺术家）
3	clerical/admin（文书/行政）
4	college/grad student（大学/研究生）
5	customer service（客户服务）
6	doctor/health care（医生/医疗保健）
7	executive/managerial（执行/管理）
8	farmer（农民）
9	homemaker（操持家务者）
10	K-12 student（K-12学生）
11	lawyer（律师）
12	programmer（程序员）
13	retired（已退休人员）
14	sales/marketing（销售/营销）
15	scientist（科学家）
16	self-employed（个体经营者）
17	technician/engineer（技术员/工程师）
18	tradesman/craftsman（商人/工匠）
19	unemployed（失业人员）
20	writer（作家）

潜在特征向量维度/维	MLP0	MLP1	MLP2	MLP3	MLP4	MLP5	MLP6	MLP7	MLP8
10	0.708 4	0.578 9	0.785 4	0.569 6	0.804 3	0.742 0	0.857 9	0.949 8	0.871 2
20	0.734 4	0.685 1	0.828 3	0.844 7	0.892 4	0.948 0	0.956 1	0.975 8	0.979 1
30	0.691 0	0.769 0	0.678 9	0.921 8	0.927 3	0.953 8	0.968 7	0.980 4	0.977 6
40	0.710 2	0.594 3	0.800 0	0.886 6	0.943 4	0.971 5	0.964 4	0.980 3	0.975 8
50	0.688 8	0.776 0	0.889 9	0.922 4	0.974 8	0.977 6	0.980 5	0.983 1	0.980 0
60	0.706 0	0.596 2	0.879 0	0.974 2	0.970 2	0.976 1	0.980 2	0.978 9	0.980 6
70	0.735 6	0.659 6	0.958 9	0.973 6	0.972 4	0.981 4	0.982 1	0.978 9	0.980 3
80	0.726 7	0.615 4	0.890 9	0.952 8	0.974 5	0.979 9	0.978 4	0.981 1	0.983 4

潜在特征向量维度/维	MLP0	MLP1	MLP2	MLP3	MLP4	MLP5	MLP6	MLP7	MLP8
10	0.605 7	0.450 5	0.727 5	0.470 0	0.757 0	0.717 0	0.843 0	0.940 9	0.860 2
20	0.651 6	0.595 7	0.785 1	0.841 9	0.881 5	0.939 9	0.948 8	0.968 8	0.972 5
30	0.596 5	0.709 0	0.625 0	0.912 0	0.919 0	0.945 3	0.960 6	0.972 5	0.969 0
40	0.627 0	0.499 3	0.774 2	0.871 0	0.933 7	0.962 7	0.956 0	0.972 7	0.969 1
50	0.612 8	0.741 6	0.874 8	0.913 3	0.967 33	0.969 9	0.973 1	0.974 9	0.973 5
60	0.640 7	0.508 1	0.864 4	0.966 7	0.962 5	0.968 2	0.973 8	0.972 2	0.974 3
70	0.673 6	0.599 8	0.948 9	0.968 3	0.965 0	0.973 1	0.975 1	0.971 5	0.973 5
80	0.672 7	0.542 1	0.872 4	0.944 3	0.967 8	0.973 6	0.971 4	0.974 3	0.975 5

Personalized recommendation model based on users multi-features fusion

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