计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (7): 1529-1542.DOI: 10.3778/j.issn.1673-9418.2101032
王雪纯1, 吕晟凯1, 吴浩2, 何鹏1,3,+(), 曾诚1
收稿日期:
2021-01-07
修回日期:
2021-03-04
出版日期:
2022-07-01
发布日期:
2021-03-12
作者简介:
王雪纯(1996—),女,湖北十堰人,硕士研究生,主要研究方向为表示学习、神经网络、服务计算。 基金资助:
WANG Xuechun1, LYU Shengkai1, WU Hao2, HE Peng1,3,+(), ZENG Cheng1
Received:
2021-01-07
Revised:
2021-03-04
Online:
2022-07-01
Published:
2021-03-12
Supported by:
摘要:
网络嵌入是将网络节点投影到一个向量空间,从而有效地提取网络中各节点的特征信息。在服务推荐领域,已有研究表明引入网络嵌入方法能有效缓解推荐过程中数据稀疏等问题。但现有的网络嵌入方法多针对某一种特定结构的网络,并没有从根源上协同多种关系网络。因此,从垂直和平行两个角度将多种关系网络映射到同一个向量空间,提出一种基于多网络混合嵌入的服务推荐模型(MNHER)。首先,构建用户社交关系网络、服务标签共有网络、用户-服务异质信息网络;然后,通过多网络混合嵌入学习,得到用户和服务在同一向量空间的嵌入向量;最后,应用用户和服务的表征向量向目标用户推荐服务。此外,也对嵌入学习中的随机游走方法进行了优化,确保能更有效地提取和保留原网络的特征信息。为验证该方法的有效性,在三个公开数据集上与多种代表性的服务推荐方法进行了对比分析,相比基于单一关系网络和简单融合多关系网络的服务推荐方法,F-measure值分别可提高21%、15%。实验结果证明了多网络混合嵌入方法可有效地协同多关系网络,提高服务推荐质量。
中图分类号:
王雪纯, 吕晟凯, 吴浩, 何鹏, 曾诚. 多网络混合嵌入学习的服务推荐方法研究[J]. 计算机科学与探索, 2022, 16(7): 1529-1542.
WANG Xuechun, LYU Shengkai, WU Hao, HE Peng, ZENG Cheng. Research on Service Recommendation Method of Multi-network Hybrid Embed-ding Learning[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(7): 1529-1542.
名称 | 用户 | 服务 | 评分数据 | 社交关系 |
---|---|---|---|---|
Douban Movie | 13 367 | 12 677 | 1 068 278 | 4 085 |
Yelp | 16 239 | 14 284 | 198 397 | 158 590 |
Movielens | 1 370 | 2 682 | 100 000 | 47 150 |
表1 实验数据集
Table 1 Experimental dataset
名称 | 用户 | 服务 | 评分数据 | 社交关系 |
---|---|---|---|---|
Douban Movie | 13 367 | 12 677 | 1 068 278 | 4 085 |
Yelp | 16 239 | 14 284 | 198 397 | 158 590 |
Movielens | 1 370 | 2 682 | 100 000 | 47 150 |
名称 | 网络 | 节点数 | 连边数 |
---|---|---|---|
Douban Movie | 用户同质信息网络 | 10 279 | 78 311 |
服务同质信息网络 | 8 207 | 63 483 | |
用户社交网络 | 9 032 | 3 357 | |
Yelp | 同质信息网络 | 13 279 | 87 137 |
服务同质信息网络 | 12 540 | 69 257 | |
用户社交网络 | 14 367 | 98 573 | |
Movielens | 用户同质信息网络 | 1 170 | 23 367 |
服务同质信息网络 | 2 194 | 17 621 | |
用户社交网络 | 1 037 | 45 889 |
表2 信息网络数据统计情况
Table 2 Data statistics of information network
名称 | 网络 | 节点数 | 连边数 |
---|---|---|---|
Douban Movie | 用户同质信息网络 | 10 279 | 78 311 |
服务同质信息网络 | 8 207 | 63 483 | |
用户社交网络 | 9 032 | 3 357 | |
Yelp | 同质信息网络 | 13 279 | 87 137 |
服务同质信息网络 | 12 540 | 69 257 | |
用户社交网络 | 14 367 | 98 573 | |
Movielens | 用户同质信息网络 | 1 170 | 23 367 |
服务同质信息网络 | 2 194 | 17 621 | |
用户社交网络 | 1 037 | 45 889 |
Dataset | Train rate | Metrics | UPCC | BPR | FMHIN | Sorec | SemRec | MVIR | CPLR | MNHER |
---|---|---|---|---|---|---|---|---|---|---|
Yelp | 80% | F1@10 | 0.148 | 0.124 | 0.189 | 0.180 | 0.216 | 0.295 | 0.317 | 0.396 |
NDGG@10 | 0.146 | 0.132 | 0.190 | 0.190 | 0.206 | 0.244 | 0.270 | 0.271 | ||
MAP@10 | 0.167 | 0.076 | 0.132 | 0.165 | 0.209 | 0.350 | 0.254 | 0.358 | ||
MRR@10 | 0.393 | 0.190 | 0.273 | 0.260 | 0.382 | 0.503 | 0.442 | 0.552 | ||
60% | F1@10 | 0.145 | 0.122 | 0.156 | 0.177 | 0.189 | 0.289 | 0.321 | 0.389 | |
NDGG@10 | 0.143 | 0.129 | 0.157 | 0.186 | 0.202 | 0.217 | 0.204 | 0.243 | ||
MAP@10 | 0.151 | 0.075 | 0.067 | 0.064 | 0.117 | 0.164 | 0.221 | 0.289 | ||
MRR@10 | 0.386 | 0.186 | 0.552 | 0.549 | 0.483 | 0.625 | 0.515 | 0.631 | ||
40% | F1@10 | 0.136 | 0.118 | 0.149 | 0.171 | 0.184 | 0.234 | 0.311 | 0.377 | |
NDGG@10 | 0.138 | 0.125 | 0.182 | 0.181 | 0.195 | 0.240 | 0.216 | 0.258 | ||
MAP@10 | 0.134 | 0.073 | 0.064 | 0.062 | 0.113 | 0.207 | 0.216 | 0.283 | ||
MRR@10 | 0.374 | 0.181 | 0.526 | 0.532 | 0.468 | 0.530 | 0.534 | 0.680 | ||
20% | F1@10 | 0.132 | 1.104 | 0.164 | 0.161 | 0.172 | 0.262 | 0.292 | 0.353 | |
NDGG@10 | 0.130 | 0.117 | 0.103 | 0.108 | 0.183 | 0.219 | 0.183 | 0.241 | ||
MAP@10 | 0.083 | 0.068 | 0.060 | 0.058 | 0.106 | 0.113 | 0.220 | 0.263 | ||
MRR@10 | 0.350 | 0.169 | 0.458 | 0.499 | 0.438 | 0.500 | 0.417 | 0.508 | ||
Douban Movie | 80% | F1@10 | 0.108 | 0.119 | 0.152 | 0.150 | 0.170 | 0.260 | 0.345 | 0.319 |
NDGG@10 | 0.118 | 0.043 | 0.257 | 0.272 | 0.173 | 0.222 | 0.393 | 0.428 | ||
MAP@10 | 0.095 | 0.114 | 0.071 | 0.075 | 0.105 | 0.186 | 0.161 | 0.239 | ||
MRR@10 | 0.494 | 0.242 | 0.439 | 0.447 | 0.552 | 0.605 | 0.524 | 0.655 | ||
60% | F1@10 | 0.131 | 0.113 | 0.143 | 0.143 | 0.161 | 0.353 | 0.337 | 0.299 | |
NDGG@10 | 0.112 | 0.041 | 0.214 | 0.258 | 0.165 | 0.190 | 0.199 | 0.236 | ||
MAP@10 | 0.072 | 0.108 | 0.051 | 0.072 | 0.100 | 0.170 | 0.135 | 0.194 | ||
MRR@10 | 0.316 | 0.235 | 0.331 | 0.336 | 0.426 | 0.502 | 0.492 | 0.615 | ||
40% | F1@10 | 0.086 | 0.106 | 0.156 | 0.149 | 0.160 | 0.293 | 0.296 | 0.338 | |
NDGG@10 | 0.100 | 0.192 | 0.052 | 0.054 | 0.163 | 0.199 | 0.345 | 0.406 | ||
MAP@10 | 0.089 | 0.053 | 0.144 | 0.142 | 0.099 | 0.168 | 0.173 | 0.192 | ||
MRR@10 | 0.384 | 0.225 | 0.365 | 0.416 | 0.513 | 0.632 | 0.554 | 0.693 | ||
20% | F1@10 | 0.177 | 0.101 | 0.145 | 0.142 | 0.153 | 0.281 | 0.238 | 0.327 | |
NDGG@10 | 0.095 | 0.184 | 0.047 | 0.052 | 0.156 | 0.211 | 0.196 | 0.245 | ||
MAP@10 | 0.085 | 0.051 | 0.130 | 0.136 | 0.095 | 0.169 | 0.147 | 0.184 | ||
MRR@10 | 0.393 | 0.215 | 0.318 | 0.398 | 0.491 | 0.604 | 0.516 | 0.633 | ||
Movielens | 80% | F1@10 | 0.105 | 0.112 | 0.111 | 0.119 | 0.143 | 0.209 | 0.205 | 0.282 |
NDGG@10 | 0.109 | 0.105 | 0.103 | 0.108 | 0.102 | 0.179 | 0.206 | 0.257 | ||
MAP@10 | 0.059 | 0.056 | 0.042 | 0.046 | 0.062 | 0.106 | 0.109 | 0.136 | ||
MRR@10 | 0.169 | 0.181 | 0.208 | 0.293 | 0.238 | 0.293 | 0.272 | 0.469 | ||
60% | F1@10 | 0.107 | 0.109 | 0.114 | 0.116 | 0.071 | 0.123 | 0.229 | 0.273 | |
NDGG@10 | 0.076 | 0.102 | 0.109 | 0.105 | 0.061 | 0.215 | 0.180 | 0.250 | ||
MAP@10 | 0.033 | 0.054 | 0.047 | 0.045 | 0.030 | 0.068 | 0.125 | 0.132 | ||
MRR@10 | 0.167 | 0.172 | 0.227 | 0.279 | 0.226 | 0.230 | 0.259 | 0.446 | ||
40% | F1@10 | 0.136 | 0.104 | 0.106 | 0.111 | 0.068 | 0.194 | 0.220 | 0.262 | |
NDGG@10 | 0.109 | 0.098 | 0.094 | 0.103 | 0.059 | 0.138 | 0.192 | 0.239 | ||
MAP@10 | 0.037 | 0.056 | 0.042 | 0.045 | 0.030 | 0.063 | 0.109 | 0.149 | ||
MRR@10 | 0.159 | 0.207 | 0.216 | 0.227 | 0.224 | 0.291 | 0.237 | 0.442 | ||
20% | F1@10 | 0.126 | 0.100 | 0.103 | 0.106 | 0.065 | 0.186 | 0.221 | 0.251 | |
NDGG@10 | 0.098 | 0.093 | 0.082 | 0.096 | 0.056 | 0.181 | 0.193 | 0.229 | ||
MAP@10 | 0.046 | 0.047 | 0.050 | 0.038 | 0.025 | 0.091 | 0.091 | 0.113 | ||
MRR@10 | 0.152 | 0.198 | 0.202 | 0.217 | 0.214 | 0.279 | 0.227 | 0.422 |
表3 不同模型在三个数据集上的推荐质量比较
Table 3 Comparison of recommendation quality of different models on three datasets
Dataset | Train rate | Metrics | UPCC | BPR | FMHIN | Sorec | SemRec | MVIR | CPLR | MNHER |
---|---|---|---|---|---|---|---|---|---|---|
Yelp | 80% | F1@10 | 0.148 | 0.124 | 0.189 | 0.180 | 0.216 | 0.295 | 0.317 | 0.396 |
NDGG@10 | 0.146 | 0.132 | 0.190 | 0.190 | 0.206 | 0.244 | 0.270 | 0.271 | ||
MAP@10 | 0.167 | 0.076 | 0.132 | 0.165 | 0.209 | 0.350 | 0.254 | 0.358 | ||
MRR@10 | 0.393 | 0.190 | 0.273 | 0.260 | 0.382 | 0.503 | 0.442 | 0.552 | ||
60% | F1@10 | 0.145 | 0.122 | 0.156 | 0.177 | 0.189 | 0.289 | 0.321 | 0.389 | |
NDGG@10 | 0.143 | 0.129 | 0.157 | 0.186 | 0.202 | 0.217 | 0.204 | 0.243 | ||
MAP@10 | 0.151 | 0.075 | 0.067 | 0.064 | 0.117 | 0.164 | 0.221 | 0.289 | ||
MRR@10 | 0.386 | 0.186 | 0.552 | 0.549 | 0.483 | 0.625 | 0.515 | 0.631 | ||
40% | F1@10 | 0.136 | 0.118 | 0.149 | 0.171 | 0.184 | 0.234 | 0.311 | 0.377 | |
NDGG@10 | 0.138 | 0.125 | 0.182 | 0.181 | 0.195 | 0.240 | 0.216 | 0.258 | ||
MAP@10 | 0.134 | 0.073 | 0.064 | 0.062 | 0.113 | 0.207 | 0.216 | 0.283 | ||
MRR@10 | 0.374 | 0.181 | 0.526 | 0.532 | 0.468 | 0.530 | 0.534 | 0.680 | ||
20% | F1@10 | 0.132 | 1.104 | 0.164 | 0.161 | 0.172 | 0.262 | 0.292 | 0.353 | |
NDGG@10 | 0.130 | 0.117 | 0.103 | 0.108 | 0.183 | 0.219 | 0.183 | 0.241 | ||
MAP@10 | 0.083 | 0.068 | 0.060 | 0.058 | 0.106 | 0.113 | 0.220 | 0.263 | ||
MRR@10 | 0.350 | 0.169 | 0.458 | 0.499 | 0.438 | 0.500 | 0.417 | 0.508 | ||
Douban Movie | 80% | F1@10 | 0.108 | 0.119 | 0.152 | 0.150 | 0.170 | 0.260 | 0.345 | 0.319 |
NDGG@10 | 0.118 | 0.043 | 0.257 | 0.272 | 0.173 | 0.222 | 0.393 | 0.428 | ||
MAP@10 | 0.095 | 0.114 | 0.071 | 0.075 | 0.105 | 0.186 | 0.161 | 0.239 | ||
MRR@10 | 0.494 | 0.242 | 0.439 | 0.447 | 0.552 | 0.605 | 0.524 | 0.655 | ||
60% | F1@10 | 0.131 | 0.113 | 0.143 | 0.143 | 0.161 | 0.353 | 0.337 | 0.299 | |
NDGG@10 | 0.112 | 0.041 | 0.214 | 0.258 | 0.165 | 0.190 | 0.199 | 0.236 | ||
MAP@10 | 0.072 | 0.108 | 0.051 | 0.072 | 0.100 | 0.170 | 0.135 | 0.194 | ||
MRR@10 | 0.316 | 0.235 | 0.331 | 0.336 | 0.426 | 0.502 | 0.492 | 0.615 | ||
40% | F1@10 | 0.086 | 0.106 | 0.156 | 0.149 | 0.160 | 0.293 | 0.296 | 0.338 | |
NDGG@10 | 0.100 | 0.192 | 0.052 | 0.054 | 0.163 | 0.199 | 0.345 | 0.406 | ||
MAP@10 | 0.089 | 0.053 | 0.144 | 0.142 | 0.099 | 0.168 | 0.173 | 0.192 | ||
MRR@10 | 0.384 | 0.225 | 0.365 | 0.416 | 0.513 | 0.632 | 0.554 | 0.693 | ||
20% | F1@10 | 0.177 | 0.101 | 0.145 | 0.142 | 0.153 | 0.281 | 0.238 | 0.327 | |
NDGG@10 | 0.095 | 0.184 | 0.047 | 0.052 | 0.156 | 0.211 | 0.196 | 0.245 | ||
MAP@10 | 0.085 | 0.051 | 0.130 | 0.136 | 0.095 | 0.169 | 0.147 | 0.184 | ||
MRR@10 | 0.393 | 0.215 | 0.318 | 0.398 | 0.491 | 0.604 | 0.516 | 0.633 | ||
Movielens | 80% | F1@10 | 0.105 | 0.112 | 0.111 | 0.119 | 0.143 | 0.209 | 0.205 | 0.282 |
NDGG@10 | 0.109 | 0.105 | 0.103 | 0.108 | 0.102 | 0.179 | 0.206 | 0.257 | ||
MAP@10 | 0.059 | 0.056 | 0.042 | 0.046 | 0.062 | 0.106 | 0.109 | 0.136 | ||
MRR@10 | 0.169 | 0.181 | 0.208 | 0.293 | 0.238 | 0.293 | 0.272 | 0.469 | ||
60% | F1@10 | 0.107 | 0.109 | 0.114 | 0.116 | 0.071 | 0.123 | 0.229 | 0.273 | |
NDGG@10 | 0.076 | 0.102 | 0.109 | 0.105 | 0.061 | 0.215 | 0.180 | 0.250 | ||
MAP@10 | 0.033 | 0.054 | 0.047 | 0.045 | 0.030 | 0.068 | 0.125 | 0.132 | ||
MRR@10 | 0.167 | 0.172 | 0.227 | 0.279 | 0.226 | 0.230 | 0.259 | 0.446 | ||
40% | F1@10 | 0.136 | 0.104 | 0.106 | 0.111 | 0.068 | 0.194 | 0.220 | 0.262 | |
NDGG@10 | 0.109 | 0.098 | 0.094 | 0.103 | 0.059 | 0.138 | 0.192 | 0.239 | ||
MAP@10 | 0.037 | 0.056 | 0.042 | 0.045 | 0.030 | 0.063 | 0.109 | 0.149 | ||
MRR@10 | 0.159 | 0.207 | 0.216 | 0.227 | 0.224 | 0.291 | 0.237 | 0.442 | ||
20% | F1@10 | 0.126 | 0.100 | 0.103 | 0.106 | 0.065 | 0.186 | 0.221 | 0.251 | |
NDGG@10 | 0.098 | 0.093 | 0.082 | 0.096 | 0.056 | 0.181 | 0.193 | 0.229 | ||
MAP@10 | 0.046 | 0.047 | 0.050 | 0.038 | 0.025 | 0.091 | 0.091 | 0.113 | ||
MRR@10 | 0.152 | 0.198 | 0.202 | 0.217 | 0.214 | 0.279 | 0.227 | 0.422 |
Method | Yelp | Douban Movie | Movielens | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
F1 | NDGG | MAP | MRR | F1 | NDGG | MAP | MRR | F1 | NDGG | MAP | MRR | |
| 0.339 | 0.255 | 0.306 | 0.470 | 0.310 | 0.381 | 0.206 | 0.653 | 0.239 | 0.220 | 0.118 | 0.346 |
| 0.354 | 0.267 | 0.320 | 0.491 | 0.329 | 0.368 | 0.216 | 0.683 | 0.250 | 0.230 | 0.123 | 0.392 |
| 0.382 | 0.287 | 0.370 | 0.546 | 0.326 | 0.412 | 0.203 | 0.659 | 0.272 | 0.258 | 0.140 | 0.427 |
| 0.396 | 0.285 | 0.376 | 0.580 | 0.345 | 0.380 | 0.251 | 0.677 | 0.096 | 0.270 | 0.133 | 0.493 |
表4 考虑不同关系的实验结果对比
Table 4 Comparison of experimental results considering different relations
Method | Yelp | Douban Movie | Movielens | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
F1 | NDGG | MAP | MRR | F1 | NDGG | MAP | MRR | F1 | NDGG | MAP | MRR | |
| 0.339 | 0.255 | 0.306 | 0.470 | 0.310 | 0.381 | 0.206 | 0.653 | 0.239 | 0.220 | 0.118 | 0.346 |
| 0.354 | 0.267 | 0.320 | 0.491 | 0.329 | 0.368 | 0.216 | 0.683 | 0.250 | 0.230 | 0.123 | 0.392 |
| 0.382 | 0.287 | 0.370 | 0.546 | 0.326 | 0.412 | 0.203 | 0.659 | 0.272 | 0.258 | 0.140 | 0.427 |
| 0.396 | 0.285 | 0.376 | 0.580 | 0.345 | 0.380 | 0.251 | 0.677 | 0.096 | 0.270 | 0.133 | 0.493 |
Method | Yelp | Douban Movie | Movielens | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
F1 | NDGG | MAP | MRR | F1 | NDGG | MAP | MRR | F1 | NDGG | MAP | MRR | ||
NULL | 0.322 | 0.243 | 0.291 | 0.447 | 0.295 | 0.267 | 0.196 | 0.621 | 0.227 | 0.209 | 0.141 | 0.329 | |
BC | 0.339 | 0.255 | 0.306 | 0.470 | 0.310 | 0.281 | 0.206 | 0.653 | 0.239 | 0.220 | 0.148 | 0.346 | |
EC | 0.275 | 0.261 | 0.336 | 0.496 | 0.297 | 0.375 | 0.185 | 0.599 | 0.247 | 0.298 | 0.128 | 0.400 | |
PR | 0.416 | 0.285 | 0.376 | 0.480 | 0.330 | 0.450 | 0.251 | 0.688 | 0.296 | 0.270 | 0.143 | 0.493 |
表5 不同中心性度量方法下的实验结果
Table 5 Experimental results under different centrality measurement methods
Method | Yelp | Douban Movie | Movielens | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
F1 | NDGG | MAP | MRR | F1 | NDGG | MAP | MRR | F1 | NDGG | MAP | MRR | ||
NULL | 0.322 | 0.243 | 0.291 | 0.447 | 0.295 | 0.267 | 0.196 | 0.621 | 0.227 | 0.209 | 0.141 | 0.329 | |
BC | 0.339 | 0.255 | 0.306 | 0.470 | 0.310 | 0.281 | 0.206 | 0.653 | 0.239 | 0.220 | 0.148 | 0.346 | |
EC | 0.275 | 0.261 | 0.336 | 0.496 | 0.297 | 0.375 | 0.185 | 0.599 | 0.247 | 0.298 | 0.128 | 0.400 | |
PR | 0.416 | 0.285 | 0.376 | 0.480 | 0.330 | 0.450 | 0.251 | 0.688 | 0.296 | 0.270 | 0.143 | 0.493 |
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