Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (6): 1719-1729.DOI: 10.11772/j.issn.1001-9081.2022060860
Special Issue: CCF第37届中国计算机应用大会 (CCF NCCA 2022)
• The 37 CCF National Conference of Computer Applications (CCF NCCA 2022) • Previous Articles Next Articles
Nannan SUN1,2, Chunhui PIAO1,3(), Xinna MA1,3
Received:
2022-06-15
Revised:
2022-07-08
Accepted:
2022-07-25
Online:
2022-10-11
Published:
2023-06-10
Contact:
Chunhui PIAO
About author:
SUN Nannan, born in 1997, M. S. Her research interests include big data, recommendation algorithm.Supported by:
通讯作者:
朴春慧
作者简介:
孙男男(1997—),女,河北衡水人,硕士,主要研究方向:大数据、推荐算法;基金资助:
CLC Number:
Nannan SUN, Chunhui PIAO, Xinna MA. Group buying recommendation method based on social relationship and time-series information[J]. Journal of Computer Applications, 2023, 43(6): 1719-1729.
孙男男, 朴春慧, 马新娜. 基于社交关系和时序信息的团购推荐方法[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1719-1729.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060860
数据集名称 | 用户数 | 项目数 | 交互数/106 | 用户平均交互数 |
---|---|---|---|---|
MovieLens-1M | 6 040 | 3 416 | 0.987 | 163.50 |
Amazon Beauty | 51 369 | 19 369 | 0.225 | 4.39 |
Tab. 1 Statistical characteristics of MovieLens-1M and Amazon Beauty datasets
数据集名称 | 用户数 | 项目数 | 交互数/106 | 用户平均交互数 |
---|---|---|---|---|
MovieLens-1M | 6 040 | 3 416 | 0.987 | 163.50 |
Amazon Beauty | 51 369 | 19 369 | 0.225 | 4.39 |
数据集 | 模型 | Pre@10 | Recall@10 | HR@10 | NDCG@10 |
---|---|---|---|---|---|
MovieLens- 1M | FPMC | 0.700 3 | 0.528 0 | 0.698 4 | 0.462 7 |
GRU4Rec | 0.754 2 | 0.541 9 | 0.739 4 | 0.513 8 | |
TiSASRec | 0.793 8 | 0.577 7 | 0.784 6 | 0.567 4 | |
Caser | 0.763 1 | 0.564 8 | 0.759 1 | 0.521 9 | |
RTSA | 0.816 9 | 0.592 8 | 0.827 9 | 0.600 2 | |
Amazon Beauty | FPMC | 0.312 4 | 0.237 0 | 0.317 4 | 0.256 8 |
GRU4Rec | 0.307 5 | 0.228 1 | 0.228 1 | 0.244 3 | |
TiSASRec | 0.335 7 | 0.241 1 | 0.331 5 | 0.266 5 | |
Caser | 0.324 5 | 0.224 1 | 0.320 6 | 0.237 8 | |
RTSA | 0.370 4 | 0.255 5 | 0.370 4 | 0.287 0 |
Tab. 2 Comparison results of five recommendation models on two datasets
数据集 | 模型 | Pre@10 | Recall@10 | HR@10 | NDCG@10 |
---|---|---|---|---|---|
MovieLens- 1M | FPMC | 0.700 3 | 0.528 0 | 0.698 4 | 0.462 7 |
GRU4Rec | 0.754 2 | 0.541 9 | 0.739 4 | 0.513 8 | |
TiSASRec | 0.793 8 | 0.577 7 | 0.784 6 | 0.567 4 | |
Caser | 0.763 1 | 0.564 8 | 0.759 1 | 0.521 9 | |
RTSA | 0.816 9 | 0.592 8 | 0.827 9 | 0.600 2 | |
Amazon Beauty | FPMC | 0.312 4 | 0.237 0 | 0.317 4 | 0.256 8 |
GRU4Rec | 0.307 5 | 0.228 1 | 0.228 1 | 0.244 3 | |
TiSASRec | 0.335 7 | 0.241 1 | 0.331 5 | 0.266 5 | |
Caser | 0.324 5 | 0.224 1 | 0.320 6 | 0.237 8 | |
RTSA | 0.370 4 | 0.255 5 | 0.370 4 | 0.287 0 |
数据集 | 模型是否具有个性化时间间隔因素 | NDCG@10 | HR@10 |
---|---|---|---|
MovieLens- 1M | 有 | 0.600 2 | 0.827 9 |
无 | 0.542 4 | 0.772 9 | |
Amazon Beauty | 有 | 0.324 1 | 0.403 9 |
无 | 0.262 2 | 0.378 5 |
Tab. 3 Influence of time interval factor on different models
数据集 | 模型是否具有个性化时间间隔因素 | NDCG@10 | HR@10 |
---|---|---|---|
MovieLens- 1M | 有 | 0.600 2 | 0.827 9 |
无 | 0.542 4 | 0.772 9 | |
Amazon Beauty | 有 | 0.324 1 | 0.403 9 |
无 | 0.262 2 | 0.378 5 |
数据集 | 用户数 | 项目数 | 群组数 | 关注者数 | 用户-项目交互数 | 群组-项目交互数 | 用户-关注者交互数 |
---|---|---|---|---|---|---|---|
MaFengWo | 5 275 | 1 513 | 995 | 13 076 | 39 761 | 3 595 | 53 235 |
Douban Book | 13 024 | 22 347 | 2 936 | 13 024 | 792 062 | 28 800 | 169 150 |
Tab. 4 Statistical characteristics of MaFengWo and Douban Book datasets
数据集 | 用户数 | 项目数 | 群组数 | 关注者数 | 用户-项目交互数 | 群组-项目交互数 | 用户-关注者交互数 |
---|---|---|---|---|---|---|---|
MaFengWo | 5 275 | 1 513 | 995 | 13 076 | 39 761 | 3 595 | 53 235 |
Douban Book | 13 024 | 22 347 | 2 936 | 13 024 | 792 062 | 28 800 | 169 150 |
数据集 | 模型 | HR@5 | NDCG@5 | HR@10 | NDCG@10 |
---|---|---|---|---|---|
MaFengWo | NCF | 0.465 8 | 0.350 1 | 0.602 4 | 0.403 5 |
NCF+avg | 0.464 1 | 0.348 9 | 0.600 1 | 0.402 1 | |
NCF+lm | 0.467 5 | 0.350 6 | 0.599 8 | 0.403 2 | |
NCF+ms | 0.458 7 | 0.347 5 | 0.597 7 | 0.398 0 | |
NCF+exp | 0.459 9 | 0.349 2 | 0.601 2 | 0.400 3 | |
SIGR | 0.468 6 | 0.356 7 | 0.608 6 | 0.414 2 | |
AGREE | 0.472 7 | 0.357 3 | 0.613 0 | 0.415 6 | |
SSAGR | 0.489 4 | 0.366 7 | 0.624 1 | 0.424 6 | |
Douban Book | NCF | 0.565 8 | 0.365 1 | 0.742 4 | 0.428 5 |
NCF+avg | 0.560 1 | 0.363 9 | 0.740 1 | 0.427 2 | |
NCF+lm | 0.555 5 | 0.365 6 | 0.739 8 | 0.428 1 | |
NCF+ms | 0.542 7 | 0.362 8 | 0.737 7 | 0.423 0 | |
NCF+exp | 0.558 9 | 0.364 2 | 0.741 2 | 0.425 3 | |
SIGR | 0.568 4 | 0.371 1 | 0.748 5 | 0.430 5 | |
AGREE | 0.572 7 | 0.372 3 | 0.753 0 | 0.440 6 | |
SSAGR | 0.589 4 | 0.381 7 | 0.764 2 | 0.449 6 |
Tab. 5 Performance comparison of recommendation models on two datasets
数据集 | 模型 | HR@5 | NDCG@5 | HR@10 | NDCG@10 |
---|---|---|---|---|---|
MaFengWo | NCF | 0.465 8 | 0.350 1 | 0.602 4 | 0.403 5 |
NCF+avg | 0.464 1 | 0.348 9 | 0.600 1 | 0.402 1 | |
NCF+lm | 0.467 5 | 0.350 6 | 0.599 8 | 0.403 2 | |
NCF+ms | 0.458 7 | 0.347 5 | 0.597 7 | 0.398 0 | |
NCF+exp | 0.459 9 | 0.349 2 | 0.601 2 | 0.400 3 | |
SIGR | 0.468 6 | 0.356 7 | 0.608 6 | 0.414 2 | |
AGREE | 0.472 7 | 0.357 3 | 0.613 0 | 0.415 6 | |
SSAGR | 0.489 4 | 0.366 7 | 0.624 1 | 0.424 6 | |
Douban Book | NCF | 0.565 8 | 0.365 1 | 0.742 4 | 0.428 5 |
NCF+avg | 0.560 1 | 0.363 9 | 0.740 1 | 0.427 2 | |
NCF+lm | 0.555 5 | 0.365 6 | 0.739 8 | 0.428 1 | |
NCF+ms | 0.542 7 | 0.362 8 | 0.737 7 | 0.423 0 | |
NCF+exp | 0.558 9 | 0.364 2 | 0.741 2 | 0.425 3 | |
SIGR | 0.568 4 | 0.371 1 | 0.748 5 | 0.430 5 | |
AGREE | 0.572 7 | 0.372 3 | 0.753 0 | 0.440 6 | |
SSAGR | 0.589 4 | 0.381 7 | 0.764 2 | 0.449 6 |
地点 | 模型 | 成员用户 | R | ||
---|---|---|---|---|---|
U837 | U838 | U839 | |||
I54 | SSAGR | 0.273 1 | 0.346 5 | 0.461 2 | 0.583 6 |
SSAGR-F | 0.333 3 | 0.333 3 | 0.333 3 | 0.276 4 | |
I284 | SSAGR | 0.258 1 | 0.507 3 | 0.218 5 | 0.374 7 |
SSAGR-F | 0.333 3 | 0.333 3 | 0.333 3 | 0.194 2 | |
I462 | SSAGR | 0.353 8 | 0.291 7 | 0.324 2 | 0.452 1 |
SSAGR-F | 0.333 3 | 0.333 3 | 0.333 3 | 0.212 4 | |
I52 | SSAGR | 0.436 1 | 0.371 2 | 0.293 7 | 0.075 0 |
SSAGR-F | 0.333 3 | 0.333 3 | 0.333 3 | 0.150 0 | |
I346 | SSAGR | 0.318 3 | 0.329 2 | 0.270 8 | 0.063 1 |
SSAGR-F | 0.333 3 | 0.333 3 | 0.333 3 | 0.129 5 | |
I591 | SSAGR | 0.295 7 | 0.407 2 | 0.311 3 | 0.058 6 |
SSAGR-F | 0.333 3 | 0.333 3 | 0.333 3 | 0.113 4 |
Tab. 6 Influence of sampled groups on group-level self-attention
地点 | 模型 | 成员用户 | R | ||
---|---|---|---|---|---|
U837 | U838 | U839 | |||
I54 | SSAGR | 0.273 1 | 0.346 5 | 0.461 2 | 0.583 6 |
SSAGR-F | 0.333 3 | 0.333 3 | 0.333 3 | 0.276 4 | |
I284 | SSAGR | 0.258 1 | 0.507 3 | 0.218 5 | 0.374 7 |
SSAGR-F | 0.333 3 | 0.333 3 | 0.333 3 | 0.194 2 | |
I462 | SSAGR | 0.353 8 | 0.291 7 | 0.324 2 | 0.452 1 |
SSAGR-F | 0.333 3 | 0.333 3 | 0.333 3 | 0.212 4 | |
I52 | SSAGR | 0.436 1 | 0.371 2 | 0.293 7 | 0.075 0 |
SSAGR-F | 0.333 3 | 0.333 3 | 0.333 3 | 0.150 0 | |
I346 | SSAGR | 0.318 3 | 0.329 2 | 0.270 8 | 0.063 1 |
SSAGR-F | 0.333 3 | 0.333 3 | 0.333 3 | 0.129 5 | |
I591 | SSAGR | 0.295 7 | 0.407 2 | 0.311 3 | 0.058 6 |
SSAGR-F | 0.333 3 | 0.333 3 | 0.333 3 | 0.113 4 |
地点 | 模型 | 测试用户(U127)的社交关注者 | R | ||
---|---|---|---|---|---|
F43 | F328 | F739 | |||
I31 | SSAGR | 0.478 6 | 0.323 4 | 0.275 2 | 0.463 6 |
SSAGR-G | 0.333 3 | 0.333 3 | 0.333 3 | 0.353 1 | |
I297 | SSAGR | 0.344 2 | 0.281 7 | 0.463 3 | 0.492 0 |
SSAGR-G | 0.333 3 | 0.333 3 | 0.333 3 | 0.401 0 | |
I521 | SSAGR | 0.321 6 | 0.279 4 | 0.493 0 | 0.533 1 |
SSAGR-G | 0.333 3 | 0.333 3 | 0.333 3 | 0.474 5 | |
I81 | SSAGR | 0.275 2 | 0.396 7 | 0.301 5 | 0.045 3 |
SSAGR-G | 0.333 3 | 0.333 3 | 0.333 3 | 0.136 2 | |
I189 | SSAGR | 0.325 6 | 0.286 3 | 0.401 8 | 0.079 5 |
SSAGR-G | 0.333 3 | 0.333 3 | 0.333 3 | 0.152 4 | |
I542 | SSAGR | 0.419 3 | 0.237 1 | 0.252 6 | 0.063 3 |
SSAGR-G | 0.333 3 | 0.333 3 | 0.333 3 | 0.121 4 |
Tab. 7 Influence of sampled users on user-level self-attention
地点 | 模型 | 测试用户(U127)的社交关注者 | R | ||
---|---|---|---|---|---|
F43 | F328 | F739 | |||
I31 | SSAGR | 0.478 6 | 0.323 4 | 0.275 2 | 0.463 6 |
SSAGR-G | 0.333 3 | 0.333 3 | 0.333 3 | 0.353 1 | |
I297 | SSAGR | 0.344 2 | 0.281 7 | 0.463 3 | 0.492 0 |
SSAGR-G | 0.333 3 | 0.333 3 | 0.333 3 | 0.401 0 | |
I521 | SSAGR | 0.321 6 | 0.279 4 | 0.493 0 | 0.533 1 |
SSAGR-G | 0.333 3 | 0.333 3 | 0.333 3 | 0.474 5 | |
I81 | SSAGR | 0.275 2 | 0.396 7 | 0.301 5 | 0.045 3 |
SSAGR-G | 0.333 3 | 0.333 3 | 0.333 3 | 0.136 2 | |
I189 | SSAGR | 0.325 6 | 0.286 3 | 0.401 8 | 0.079 5 |
SSAGR-G | 0.333 3 | 0.333 3 | 0.333 3 | 0.152 4 | |
I542 | SSAGR | 0.419 3 | 0.237 1 | 0.252 6 | 0.063 3 |
SSAGR-G | 0.333 3 | 0.333 3 | 0.333 3 | 0.121 4 |
评价指标 | RTSA | SSAGR(Group) | SSGAR(User) |
---|---|---|---|
HR@10 | 0.810 7 | 0.764 2 | 0.778 2 |
NDCG@10 | 0.589 9 | 0.449 6 | 0.460 1 |
Tab. 8 Models performance comparison on Douban Book dataset
评价指标 | RTSA | SSAGR(Group) | SSGAR(User) |
---|---|---|---|
HR@10 | 0.810 7 | 0.764 2 | 0.778 2 |
NDCG@10 | 0.589 9 | 0.449 6 | 0.460 1 |
模型 | 用户 | top-1 | top-2 | top-3 | top-4 | top-5 |
---|---|---|---|---|---|---|
RTSA | U851 | 告白 | 法医秦明 | 竹林中 | 黑色回声 | 华丽人生 |
SSAGR | U851 | 法医秦明 | 华丽人生 | 怦然心动 | 尼罗河上的惨案 | 竹林中 |
G105 | 心隐之地 | 从你的全世界路过 | 心流 | 怦然心动 | 尼罗河上的惨案 | |
G175 | 法医秦明 | 十三层空间 | 怦然心动 | 告白 | 偷影子的人 | |
G209 | 流浪地球 | 知己 | 千与千寻 | 竹林中 | 十三层空间 |
Tab. 9 Top-5 results for sampled users and groups on Douban Book dataset
模型 | 用户 | top-1 | top-2 | top-3 | top-4 | top-5 |
---|---|---|---|---|---|---|
RTSA | U851 | 告白 | 法医秦明 | 竹林中 | 黑色回声 | 华丽人生 |
SSAGR | U851 | 法医秦明 | 华丽人生 | 怦然心动 | 尼罗河上的惨案 | 竹林中 |
G105 | 心隐之地 | 从你的全世界路过 | 心流 | 怦然心动 | 尼罗河上的惨案 | |
G175 | 法医秦明 | 十三层空间 | 怦然心动 | 告白 | 偷影子的人 | |
G209 | 流浪地球 | 知己 | 千与千寻 | 竹林中 | 十三层空间 |
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