考虑多层次潜在特征的个性化推荐模型

doi:10.11959/j.issn.1000-0801.2022035

Abstract

Abstract:

Personalized recommendation has become one of the most effective means to solve information overload, and it is also a hot technology in the research field of massive data mining.However, traditional recommendation algorithms often only use the user’s rating information on the item, and lack a comprehensive consideration of the potential characteristics of the user and the item.The factorization machine, wide neural network, crossover network and deep neural network were combined to extract the shallow latent features, low-order nonlinear latent features, linear cross latent features, and high-order nonlinear latent features of users and items.Thus, a new deep learning personalized recommendation model with multilevel latent features was established.The experimental results on four commonly used data sets show that considering the multi-level potential features of users and items can effectively improve the prediction accuracy of personalized recommendations.Finally, the influence of factors such as the dimensions of the embedding layer and the number of neurons on the prediction performance of the new model was studied.

Key words: personalized recommendation, hierarchical latent feature, deep learning

CLC Number:

TP391.3

Qing SHEN, Wenbin GUO, Jungang LOU, Qiangguo YU. Personalized recommendation model with multi-level latent features[J]. Telecommunications Science, 2022, 38(2): 71-83.

Figures/Tables 16

训练集占比	80%	$70 %$	50%	30%
MovieLens-100k	0.949 6	$0 . 9559$	0.968 5	0.981 1
MovieLens-1m	0.964 3	$0 . 9687$	0.977 7	0.986 6
MovieLens-latest-small	0.986 4	$0 . 9881$	0.991 5	0.994 9
FilmTrust	0.990 9	$0 . 9920$	0.994 3	0.996 5

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数据集	数据集大小	评分区间
MovieLens-100k	10万余条评分，943位用户，1 682部电影	1～5
MovieLens-1m	100 万余条评分，6 040位用户，3 952部电影	1～5
MovieLens-l atest-small	100 836 条评分，610 位用户，9 742部电影	0.5～5
FilmTrust	35 497条评分，1 508位用户、2 071部电影	0.5～4

训练集占比	80%	70%	50%	30%
UB	0.825 4±0.022 7	0.838 5±0.013 2	0.842 9±0.017 9	0.882 3±0.027 2
FM	0.744 9±0.002 3	0.745 8±0.005 4	0.747 7±0.007 9	0.761 3±0.009 4
DNN	0.732 5±0.009 3	0.741 4±0.019 9	0.745 8±0.013 1	0.760 5±0.017 2
DeepFM	0.719 9±0.005 3	0.729 5±0.006 9	0.742 2±0.004 5	0.759 1±0.006 4
DCN	0.723 2±0.007 3	0.730 1±0.005 7	0.744 9±0.007 8	0.758 3±0.008 9
WD	0.719 4±0.005 4	0.728 9±0.004 9	0.7411±0.009 5	0.759 3±0.010 1
本文模型	0.710 2±0.004 3	0.724 9±0.006 7	0.735 2±0.007 9	0.754 7±0.005 3

训练集占比	80%	70%	50%	30%
UB	1.028 3±0.034 1	1.043 3±0.017 3	1.054 2±0.021 5	1.094 3±0.029 5
FM	0.944 1±0.003 3	0.945 3±0.007 7	0.948 5±0.009 4	0.967 5±0.013 1
DNN	0.936 3±0.011 1	0.942 7±0.014 9	0.947 4±0.006 6	0.966 1±0.009 2
DeepFM	0.920 1±0.006 9	0.928 9±0.006 1	0.938 6±0.008 6	0.959 2±0.011 7
DCN	0.922 9±0.009 4	0.931 1±0.008 3	0.941 6±0.010 9	0.959 7±0.009 1
WD	0.919 4±0.006 8	0.927 3±0.009 1	0.937 9±0.014 2	0.958 1±0.007 4
本文模型	0.909 8±0.005 3	0.926 1±0.004 3	0.931 2±0.005 5	0.952 4±0.008 5

训练集占比	80%	70%	50%	30%
UB	0.821 3±0.008 1	0.830 3±0.012 4	0.851 4±0.015 8	0.876 9±0.019 3
FM	0.674 5±0.018 2	0.675 6±0.019 6	0.706 8±0.021 4	0.734 2±0.025 1
DNN	0.692 6±0.019 7	0.701 8±0.020 3	0.719 4±0.024 6	0.738 5±0.026 8
DeepFM	0.672 8±0.007 8	0.672 7±0.009 1	0.688 4±0.012 6	0.706 8±0.013 4
DCN	0.673 6±0.014 1	0.673 3±0.015 7	0.689 5±0.017 3	0.714 5±0.016 2
WD	0.674 7±0.009 3	0.673 9±0.010 6	0.691 7±0.011 4	0.718 1±0.013 6
本文模型	0.663 5±0.011 7	0.668 1±0.012 9	0.685 2±0.013 5	0.702 3±0.015 1

训练集占比	80%	70%	50%	30%
UB	1.019 8±0.007 2	1.028 7±0.009 1	1.047 6±0.012 7	1.073 7±0.015 3
FM	0.862 3±0.017 8	0.864 7±0.019 4	0.885 4±0.021 3	0.915 8±0.023 8
DNN	0.877 4±0.015 4	0.886 4±0.018 3	0.897 2±0.020 6	0.923 8±0.026 8
DeepFM	0.856 5±0.008 7	0.862 3±0.009 5	0.873 4±0.010 7	0.899 1±0.011 3
DCN	0.857 2±0.005 7	0.863 7±0.008 6	0.875 9±0.007 4	0.903 4±0.009 1
WD	0.858 6±0.017 5	0.863 1±0.013 2	0.879 3±0.014 4	0.908 9±0.012 8
本文模型	0.849 3±0.019 7	0.857 2±0.014 5	0.872 6±0.015 9	0.891 2±0.013 7

Personalized recommendation model with multi-level latent features

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Figures/Tables 16

References 27

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训练集占比	80%	70%	50%	30%
UB	0.924 7±0.053 3	0.935 0±0.046 2	0.951 7±0.041 7	0.968 4±0.038 9
FM	0.692 6±0.015 5	0.699 8±0.017 6	0.711 8±0.019 4	0.728 3±0.021 3
DNN	0.711 7±0.023 5	0.723 9±0.031 6	0.735 6±0.033 4	0.741 2±0.036 1
DeepFM	0.684 3±0.012 7	0.688 2±0.014 2	0.701 8±0.016 7	0.711 3±0.017 8
DCN	0.680 2±0.007 6	0.679 5±0.009 4	0.695 7±0.010 3	0.708 4±0.012 7
WD	0.682 2±0.011 8	0.684 7±0.013 7	0.697 3±0.015 6	0.709 8±0.017 5
本文模型	0.671 4±0.013 6	0.673 3±0.014 6	0.690 2±0.016 2	0.701 5±0.018 4

训练集占比	80%	70%	50%	30%
UB	1.016 0±0.068 3	1.022 6±0.053 9	1.036 4±0.058 6	1.057 6±0.049 3
FM	0.893 4±0.013 6	0.897 9±0.015 7	0.905 2±0.018 7	0.917 3±0.020 4
DNN	0.901 3±0.027 2	0.916 3±0.032 8	0.928 3±0.037 1	0.937 6±0.036 8
DeepFM	0.878 9±0.017 5	0.886 1±0.018 3	0.902 5±0.020 9	0.921 6±0.021 7
DCN	0.877 8±0.009 4	0.884 7±0.010 9	0.899 4±0.012 4	0.919 6±0.013 4
WD	0.875 9±0.013 3	0.881 4±0.014 7	0.901 6±0.016 2	0.920 2±0.017 9
本文模型	0.867 2±0.015 8	0.870 4±0.016 4	0.893 7±0.017 8	0.914 5±0.019 2

训练集占比	80%	70%	50%	30%
UB	0.685 9±0.012 6	0.698 5±0.013 9	0.729 9±0.016 4	0.794 2±0.019 2
FM	0.639 1±0.013 4	0.645 9±0.014 7	0.660 8±0.017 3	0.689 3±0.018 6
DNN	0.651 2±0.022 7	0.659 2±0.025 4	0.678 4±0.026 2	0.691 5±0.029 3
DeepFM	0.630 9±0.011 9	0.634 5±0.013 5	0.661 3±0.014 9	0.674 1±0.016 1
DCN	0.633 8±0.012 5	0.637 3±0.013 9	0.665 9±0.015 7	0.678 6±0.017 3
WD	0.631 7±0.009 6	0.640 9±0.010 4	0.667 4±0.013 2	0.670 3±0.014 5
本文模型	0.621 4±0.011 3	0.634 8±0.012 6	0.655 0±0.014 8	0.665 3±0.015 8

训练集占比	80%	70%	50%	30%
UB	0.953 9±0.018 7	0.978 4±0.021 4	1.038 9±0.024 3	1.151 0±0.027 9
FM	0.831 6±0.017 3	0.846 2±0.019 8	0.857 7±0.020 7	0.889 4±0.023 2
DNN	0.839 3±0.023 1	0.843 8±0.025 5	0.866 8±0.027 1	0.898 7±0.029 6
DeepFM	0.810 3±0.011 7	0.823 1±0.012 8	0.838 9±0.013 5	0.846 7±0.015 2
DCN	0.811 7±0.010 7	0.825 6±0.011 6	0.841 9±0.012 9	0.854 6±0.014 7
WD	0.809 8±0.008 4	0.817 1±0.009 3	0.836 5±0.010 4	0.843 9±0.011 5
本文模型	0.800 2±0.010 8	0.812 4±0.011 2	0.827 4±0.012 6	0.837 6±0.013 9

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数据集	MovieLens-100k	MovieLens-1m	MovieLens-latest-small	FilmTrust
FM	68.09	230.63	54.61	13.62
DNN	66.77	273.29	57.34	20.15
DeepFM	93.61	411.64	89.38	23.81
DCN	74.82	400.74	91.97	21.39
WD	65.68	381.83	87.65	20.34
本文模型	123.41	987.35	113.47	33.84