融合基础属性和通信行为的移动用户个性化推荐

doi:10.6040/j.issn.1671-9352.0.2022.007

Abstract

Abstract:

Considering the factors which affect the mobile users' product ordering, this paper proposes a new matrix factorization model integrating the basic information and communication behavior of the mobile users, and compares the performance of the proposed method with the traditional model on the prediction of rating and Top-N recommendation. For the prediction of rating, some indices are adjusted in the RFM model by combining with the characteristics of mobile users in the product ordering process. Then a user-product rating matrix, constructed by the adjusted RFM model, can accurately and objectively reflect the users' preference for products. For the Top-N recommendation, a negative sampling method is adopted that popular products with no ordering behavior are preferred to be included in the negative samples. The numerical results show that the proposed method performs better.

Key words: mobile users, matrix factorization model, RFM model, rating prediction, Top-N recommendation

CLC Number:

TP391

Xianjun WU,Shaoshi TANG,Mingqiu WANG. Personalized recommendation of mobile users by integrating basic information and communication behavior[J].JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(9): 81-93.

Figures/Tables 16

Table 1

Table 2

Table 3

Significance of indices(p)"

用户指标	产品编号
用户指标	1	2	3	4	5
nat_age_year	0.000 8^***	5.94e-05^***	0.220	< 2e-16^***	0.265
sex_id	0.001^**	2.54e-12^***	< 2e-16^***	7.68e-06^***	< 2e-16^***
user_online	0.548	1.72e-07^***	0.770	9.61e-05^***	0.103
arpu_avg	2.05e-15^***	0.010^*	0.0003^***	0.192	0.237
arpu_std	0.064^Δ	0.029^*	2.68e-06^***	0.003^**	0.014^*
dou_avg	0.002^**	3.49e-06^***	0.986	0.212	0.319
dou_std	0.649	0.008^**	0.438	0.833	0.239
bhd_avg	0.000 7^***	0.516	0.162	0.397	0.239
bhd_std	0.000 7^***	0.570	0.141	0.330	0.213
overgprs_fee_avg	0.000 2^***	0.093^Δ	0.378	0.307	0.114
overgprs_fee_std	0.028^*	0.659	2.45e-05^***	0.193	0.000 6^***
overgprs_flag	0.004^**	0.457	< 2e-16^***	0.351	6.5e-05^***
mou_avg	0.544	0.831	0.000 2^***	0.067^Δ	0.051 1 ^Δ
mou_std	0.726	0.815	0.009^**	0.742	0.010^*
overcall_fee_avg	0.367	0.982	0.000 7^***	0.139	0.024^*
overcall_fee_std	0.018^*	0.144	0.000 5^***	0.195	0.002^**
overcall_flag	2.53e-05^***	0.000 5^***	0.076^Δ	0.352	0.004^**

用户指标	产品编号
用户指标	6	7	8	9	10
nat_age_year	0.005^**	1.09e-07^***	0.021^*	1.15e-07^***	0.001^**
sex_id	0.000 2^***	5.61e-10^***	< 2e-16^***	1.61e-12^***	5.88e-09^***
user_online	0.494	0.867	0.0004^***	0.011^*	0.003^**
arpu_avg	4.66e-14^***	0.301	0.056^Δ	0.523	1.12e-09^***
arpu_std	0.731	0.010^*	6.94e-05^***	0.166	0.513
dou_avg	0.010^**	0.000 6^***	0.922	0.001^**	0.496
dou_std	0.045^*	0.773	0.647	0.002^**	0.086^Δ
bhd_avg	0.421	0.453	0.900	0.595	0.204
bhd_std	0.410	0.780	0.864	0.664	0.191
overgprs_fee_avg	0.219	0.883	0.313	0.110	0.002^**
overgprs_fee_std	0.327	0.239	0.000 8^***	0.049^*	0.633
overgprs_flag	0.094^Δ	0.282	0.926	6.95e-10^***	0.026^*
mou_avg	9.12e-11^***	0.853	0.854	0.009^**	0.002^**
mou_std	0.844	0.010^**	0.705	0.077^Δ	0.188
overcall_fee_avg	0.271	0.180	0.088^Δ	0.084^Δ	0.323
overcall_fee_std	0.011^*	0.010^**	0.443	0.232	0.060^Δ
overcall_flag	0.035^*	0.002^**	0.235	0.035^*	0.005^**

用户指标	产品编号
用户指标	11	12	13	14	15
nat_age_year	0.125	7.56e-13^***	< 2e-16^***	2.04e-06^***	3.94e-06^***
sex_id	0.011^*	6.85e-08^***	< 2e-16^***	1.40e-06^***	< 2e-16^***
user_online	0.425	9.23e-07^***	0.107	0.213	0.279
arpu_avg	0.368	0.034^*	3.50e-06^***	0.042^*	4.35e-12^***
arpu_std	0.229	0.010^**	7.19e-06^***	0.324	0.227
dou_avg	0.073^Δ	0.210	8.77e-06^***	0.035^*	1.20e-10^***
dou_std	0.513	0.013^*	0.080^Δ	0.079^Δ	0.002^**
bhd_avg	0.619	0.599	0.018^*	0.606	0.376
bhd_std	0.728	0.628	0.039^*	0.619	0.211
overgprs_fee_avg	0.896	0.135	0.130	0.980	0.169
overgprs_fee_std	0.484	0.009^**	0.047^*	0.626	0.714
overgprs_flag	0.546	0.363	< 2e-16^***	0.0001^***	0.002^**
mou_avg	0.804	0.076^Δ	0.883	0.671	0.891
mou_std	0.973	0.266	0.974	0.429	0.581
overcall_fee_avg	0.937	0.155	0.084^Δ	0.883	0.021^*
overcall_fee_std	0.425	0.103	0.264	0.449	0.089^Δ
overcall_flag	0.234	0.195	0.905	0.001^**	2.19e-06^***

Table 3

Fig.1

Table 4

Table 5

Calculation of F"

用户编号	产品编号	订购次数c_ui	产品总使用时长t_ui	产品缴费周期T_i	F
1	1	c₁₁	t₁₁	T₁	$\frac{t_{11}}{T_1}$
1	2	c₁₂	t₁₂	T₂	$\frac{t_{12}}{T_2}$
1	3	c₁₃	-	-	c₁₃
⋮	⋮	⋮	⋮	⋮	⋮
m	n-1	c_m(n-1)	t_m(n-1)	T_n-1	$\left[\frac{t_{m(n-1)}}{T_{n-1}}\right]$
m	n	c_mn	t_mn	T_n	$\left[\frac{t_{m n}}{T_{n}}\right]$

Table 5

Table 6

Calculation of M"

用户编号	产品编号	该产品订购总金额a_ui	所有产品订购总金额A_u	M
1	1	a₁₁	A₁	$\frac{a_{11}}{A_{1}}$
1	2	a₁₂	A₁	$\frac{a_{12}}{A_{1}}$
⋮	⋮	⋮	⋮	⋮
m	n-1	a_m(n-1)	A_m	$\frac{a_{m(n-1)}}{A_{m}}$
m	n	a_mn	A_m	$\frac{a_{m n}}{A_{m}}$

Table 6

Table 7

Table 8

Table 9

Table 10

Fig.2

Fig.3

Table 11

Table 12

Table 13

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产品类型	产品编号	产品名称	产品内容示例
流量类产品	1	流量扩容	每月消费20元, 享每月2 GB国内流量扩容
	2	定向流量	9元15 GB某APP定向流量(可选择抖音、爱奇艺, 网易云等多款热门APP之一)
	3	快消包	10元5 GB国内流量, 7 d内有效
	4	假期包	暑期30元30 GB流量
	5	模组升档	升50元流量模组, 享打折优惠或共享流量产品
语音类产品	6	语音扩容	每月消费25元, 享每月100 min国内语音扩容
语音类产品	7	语音翻番包	语音翻番包10元80 min
权益类产品	8	双V会员	30元包随心选会员+咪咕视频黄金会员+30 GB定向流量
	9	车主会员	车主会员10元/月, 享免费洗车及加油打折优惠
	10	尊享会员	车主会员3元/月, 享权益汇产品优惠
	11	手机直播包	咪咕视频黄金会员+15 GB咪咕视频流量
	12	美食生活礼包	15元享美团、饿了么会员任选其一+1 GB国内通用流量
	13	长期提速包	达量限速套餐客户订购长期提速包可以解除限速
	14	视频彩铃	为主叫用户提供有趣的视频媒体代替普通网络回铃音
	15	点播年包	魔百和电影+电视剧组合年包199元

数据类型	字段名称	中文解释	来源表
用户基础属性信息	nat_age_year	用户年龄	dw_product_ageinfo_202012
	sex_id	用户性别	dw_product_ageinfo_202012
	user_online	在网时长	dw_cust_label_monthone_202012
用户通信行为信息	arpu_avg	实际消费金额均值	dw_newbusi_product_gprs
	arpu_std	实际消费金额方差	dw_newbusi_product_gprs
	dou_avg	流量均值	dw_newbusi_product_gprs
	dou_std	流量方差	dw_newbusi_product_gprs
	bhd_avg	饱和度均值	dw_product_bhd_user_mm
	bhd_std	饱和度方差	dw_product_bhd_user_mm
	dou_overfee_avg	流量超套费用均值	dw_acct_newbusi_mm
	dou_overfee_std	流量超套费用方差	dw_acct_newbusi_mm
	dou_overfee_cn	流量超套次数	dw_acct_newbusi_mm
	mou_avg	语音均值	dw_newbusi_call_overpkg
	mou_std	语音方差	dw_newbusi_call_overpkg
	mou_overfee_avg	语音超套费用均值	dw_newbusi_call_overpkg
	mou_overfee_std	语音超套费用方差	dw_newbusi_call_overpkg
	mou_overfee_cn	语音超套次数	dw_newbusi_call_overpkg
用户订购行为信息	recency	最近订购时间	dw_product_priv_accept_ds
	time	产品使用时长	dw_product_priv_accept_ds
	frequency	业务订购频率	dw_product_priv_accept_ds
	monetary	订购金额	dw_product_priv_accept_ds

指标	R	F	M
信息熵值	0.759	0.807	0.804
信息熵冗余度	0.241	0.193	0.196
权重	0.383	0.306	0.310

用户编号	产品编号	R′	F′	M′	得分
1	1	R′₁₁	F′₁₁	M′₁₁	R_weightR′₁₁+F_weightF′₁₁+M_weightM′₁₁
1	2	R′₁₂	F′₁₂	M′₁₂	R_weightR′₁₂+F_weightF′₁₂+M_weightM′₁₂
⋮	⋮	⋮	⋮	⋮	⋮
m	n-1	R′_m(n-1)	F′_m(n-1)	M′_m(n-1)	R_weightR′_m(n-1)+F_weightF′_m(n-1)+M_weightM′_m(n-1)
m	n	R′_mn	F′_mn	M′_mn	R_weightR′_mn+F_weightF′_mn+M_weightM′_mn

用户编号	产品类型
用户编号	点播年包	美食生活礼包	快消包	模组升档
1	0	0	2.827	0
2	0	0	5.354	0
3	0	0	0	4.532
4	0	0	1.920	2.975
5	0	0	6.105	0
6	3.138	0	0	0
7	2.058	0	2.232	0
8	0	0	2.721	0
9	0	3.279	5.668	0
10	0	3.465	0	2.789

Personalized recommendation of mobile users by integrating basic information and communication behavior

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用户编号	产品编号	最后一次购买时间t_ui	设定最后日期	R
1	1	t₁₁	2020-12-31	T-t₁₁
1	2	t₁₂	2020-12-31	T-t₁₂
⋮	⋮	⋮	⋮	⋮
m	n-1	t_m(n-1)	2020-12-31	T-t_m(n-1)
m	n	t_mn	2020-12-31	T-t_mn

K	传统的矩阵分解模型		改进的矩阵分解模型
K	RMSE	MAE	RMSE	MAE
2	0.350	0.255	0.182	0.141
4	0.252	0.199	0.144	0.121
6	0.196	0.166	0.124	0.114
8	0.152	0.136	0.116	0.111
10	0.147	0.135	0.113	0.111

用户编号	产品正样本	ratio=1	ratio=2
1	{3, 5}	{8, 2}	{8, 2, 4, 10}
2	{2, 10}	{5, 3}	{5, 3, 8, 4}
3	{3, 8, 10}	{5, 2, 4}	{5, 2, 4, 14, 15, 9}
4	{3, 5}	{8, 2}	{8, 2, 4, 10}
5	{3, 8}	{5, 2}	{5, 2, 4, 10}
6	{4, 8, 10}	{5, 3}	{5, 3, 2, 14, 15, 9}
7	{5, 6, 8}	{3, 2, 4}	{3, 2, 4, 10, 14, 15}
8	{4, 5}	{3, 8}	{3, 8, 2, 10}
9	{3, 4}	{5, 8}	{5, 8, 2, 10}
10	{2, 5}	{3, 8}	{3, 8, 4, 10}

模型类别	N	ratio	精确率	召回率	F₁值	覆盖率	流行度
经典的矩阵分解模型	1	1	0.031	0.043	0.036	0.400	5.167
		2	0.049	0.031	0.038	0.267	5.602
		3	0.098	0.083	0.090	0.133	5.945
	2	1	0.057	0.069	0.062	0.467	5.091
		2	0.115	0.123	0.119	0.267	5.761
		3	0.206	0.216	0.211	0.133	6.447
改进的矩阵分解模型	1	1	0.048	0.068	0.056	0.333	5.236
		2	0.060	0.045	0.051	0.200	5.639
		3	0.127	0.131	0.129	0.133	6.223
	2	1	0.070	0.082	0.076	0.333	5.316
		2	0.144	0.117	0.129	0.267	6.178
		3	0.238	0.221	0.229	0.200	6.429

用户A		用户B
推荐商品编号	兴趣度	推荐商品编号	兴趣度
6	0.817	3	0.789
3	0.804	5	0.769

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