Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (8): 2448-2455.DOI: 10.11772/j.issn.1001-9081.2022071029
Special Issue: 数据科学与技术
• Data science and technology • Previous Articles Next Articles
Shijie PENG, Hongmei CHEN, Lizhen WANG, Qing XIAO
Received:
2022-07-14
Revised:
2022-11-18
Accepted:
2022-11-30
Online:
2023-01-15
Published:
2023-08-10
Contact:
Hongmei CHEN
About author:
PENG Shijie, born in 1998, M. S. candidate. His research interests include spatial data mining.Supported by:
彭诗杰, 陈红梅, 王丽珍, 肖清
通讯作者:
陈红梅
作者简介:
彭诗杰(1998—),男,江西萍乡人,硕士研究生,主要研究方向:空间数据挖掘基金资助:
CLC Number:
Shijie PENG, Hongmei CHEN, Lizhen WANG, Qing XIAO. Hybrid point-of-interest recommendation model based on geographic preference ranking[J]. Journal of Computer Applications, 2023, 43(8): 2448-2455.
彭诗杰, 陈红梅, 王丽珍, 肖清. 基于地理偏好排序的兴趣点混合推荐模型[J]. 《计算机应用》唯一官方网站, 2023, 43(8): 2448-2455.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022071029
符号 | 描述 |
---|---|
用户集合、POI集合 | |
用户 | |
用户-POI签到矩阵 | |
用户 | |
用户 | |
POI | |
用户 | |
BPR和GWBPR模型中的用户特征矩阵、POI特征矩阵 | |
GWBPR模型中矩阵维度 | |
GWBPR模型中POI i的偏置项 | |
LMF模型中的用户表示矩阵、POI表示矩阵 | |
LMF模型中矩阵维度 | |
LMF模型中用户 |
Tab. 1 Symbols used in this paper and their descriptions
符号 | 描述 |
---|---|
用户集合、POI集合 | |
用户 | |
用户-POI签到矩阵 | |
用户 | |
用户 | |
POI | |
用户 | |
BPR和GWBPR模型中的用户特征矩阵、POI特征矩阵 | |
GWBPR模型中矩阵维度 | |
GWBPR模型中POI i的偏置项 | |
LMF模型中的用户表示矩阵、POI表示矩阵 | |
LMF模型中矩阵维度 | |
LMF模型中用户 |
数据集 | 用户数 | POI数 | 签到数 | 矩阵稀疏程度/% | 用户平均签到数 |
---|---|---|---|---|---|
Foursquare | 7 642 | 28 483 | 512 532 | 99.87 | 67.06 |
Gowalla | 5 628 | 31 803 | 620 683 | 99.78 | 110.28 |
Tab. 2 Statistics of datasets
数据集 | 用户数 | POI数 | 签到数 | 矩阵稀疏程度/% | 用户平均签到数 |
---|---|---|---|---|---|
Foursquare | 7 642 | 28 483 | 512 532 | 99.87 | 67.06 |
Gowalla | 5 628 | 31 803 | 620 683 | 99.78 | 110.28 |
指标 | PMF | MGM | LGLMF | FGCRec | BPR | LMF | SAE-NAD | GWBPR⁃LMF |
---|---|---|---|---|---|---|---|---|
Precision@5 | 0.002 6 | 0.018 2 | 0.031 8 | 0.033 3 | 0.029 3 | 0.039 4 | 0.039 5 | 0.056 9 |
Precision@10 | 0.002 5 | 0.016 6 | 0.026 4 | 0.027 8 | 0.022 0 | 0.031 6 | 0.030 5 | 0.044 2 |
Precision@15 | 0.002 3 | 0.016 6 | 0.023 3 | 0.024 4 | 0.019 0 | 0.027 5 | 0.026 1 | 0.037 7 |
Precision@20 | 0.002 0 | 0.016 1 | 0.021 3 | 0.022 7 | 0.016 9 | 0.025 0 | 0.022 8 | 0.033 4 |
Recall@5 | 0.001 9 | 0.015 7 | 0.025 7 | 0.026 6 | 0.019 7 | 0.028 8 | 0.027 1 | 0.042 3 |
Recall@10 | 0.003 5 | 0.028 2 | 0.042 5 | 0.044 1 | 0.030 0 | 0.046 8 | 0.042 0 | 0.065 7 |
Recall@15 | 0.004 8 | 0.042 3 | 0.056 3 | 0.057 5 | 0.039 3 | 0.061 3 | 0.053 6 | 0.083 9 |
Recall@20 | 0.005 8 | 0.054 1 | 0.067 5 | 0.071 0 | 0.046 8 | 0.073 7 | 0.062 1 | 0.099 0 |
F1@5 | 0.002 1 | 0.016 8 | 0.028 4 | 0.029 5 | 0.023 5 | 0.033 2 | 0.039 5 | 0.048 5 |
F1@10 | 0.002 9 | 0.020 8 | 0.032 5 | 0.034 1 | 0.025 3 | 0.037 7 | 0.030 5 | 0.052 8 |
F1@15 | 0.003 1 | 0.023 8 | 0.032 9 | 0.034 2 | 0.025 6 | 0.037 9 | 0.026 1 | 0.052 0 |
F1@20 | 0.002 9 | 0.024 8 | 0.032 3 | 0.034 4 | 0.024 8 | 0.037 3 | 0.022 8 | 0.049 9 |
NDCG@5 | 0.001 7 | 0.021 2 | 0.032 4 | 0.036 0 | 0.028 9 | 0.036 6 | 0.041 4 | 0.057 8 |
NDCG@10 | 0.001 5 | 0.019 6 | 0.028 5 | 0.031 3 | 0.024 3 | 0.031 9 | 0.034 6 | 0.048 6 |
NDCG@15 | 0.001 3 | 0.018 8 | 0.026 1 | 0.028 4 | 0.021 7 | 0.028 7 | 0.031 0 | 0.043 3 |
NDCG@20 | 0.001 3 | 0.018 2 | 0.024 4 | 0.026 7 | 0.019 9 | 0.026 7 | 0.028 3 | 0.039 7 |
mAP@5 | 0.000 5 | 0.008 9 | 0.014 1 | 0.015 7 | 0.010 0 | 0.014 4 | 0.015 4 | 0.023 7 |
mAP@10 | 0.000 6 | 0.011 2 | 0.016 9 | 0.018 6 | 0.011 7 | 0.017 1 | 0.017 8 | 0.027 7 |
mAP@15 | 0.000 6 | 0.012 6 | 0.018 5 | 0.020 0 | 0.012 6 | 0.018 3 | 0.019 1 | 0.029 7 |
mAP@20 | 0.000 7 | 0.013 6 | 0.019 4 | 0.021 1 | 0.013 2 | 0.019 3 | 0.019 9 | 0.031 1 |
Tab. 3 Experimental results on Foursquare dataset
指标 | PMF | MGM | LGLMF | FGCRec | BPR | LMF | SAE-NAD | GWBPR⁃LMF |
---|---|---|---|---|---|---|---|---|
Precision@5 | 0.002 6 | 0.018 2 | 0.031 8 | 0.033 3 | 0.029 3 | 0.039 4 | 0.039 5 | 0.056 9 |
Precision@10 | 0.002 5 | 0.016 6 | 0.026 4 | 0.027 8 | 0.022 0 | 0.031 6 | 0.030 5 | 0.044 2 |
Precision@15 | 0.002 3 | 0.016 6 | 0.023 3 | 0.024 4 | 0.019 0 | 0.027 5 | 0.026 1 | 0.037 7 |
Precision@20 | 0.002 0 | 0.016 1 | 0.021 3 | 0.022 7 | 0.016 9 | 0.025 0 | 0.022 8 | 0.033 4 |
Recall@5 | 0.001 9 | 0.015 7 | 0.025 7 | 0.026 6 | 0.019 7 | 0.028 8 | 0.027 1 | 0.042 3 |
Recall@10 | 0.003 5 | 0.028 2 | 0.042 5 | 0.044 1 | 0.030 0 | 0.046 8 | 0.042 0 | 0.065 7 |
Recall@15 | 0.004 8 | 0.042 3 | 0.056 3 | 0.057 5 | 0.039 3 | 0.061 3 | 0.053 6 | 0.083 9 |
Recall@20 | 0.005 8 | 0.054 1 | 0.067 5 | 0.071 0 | 0.046 8 | 0.073 7 | 0.062 1 | 0.099 0 |
F1@5 | 0.002 1 | 0.016 8 | 0.028 4 | 0.029 5 | 0.023 5 | 0.033 2 | 0.039 5 | 0.048 5 |
F1@10 | 0.002 9 | 0.020 8 | 0.032 5 | 0.034 1 | 0.025 3 | 0.037 7 | 0.030 5 | 0.052 8 |
F1@15 | 0.003 1 | 0.023 8 | 0.032 9 | 0.034 2 | 0.025 6 | 0.037 9 | 0.026 1 | 0.052 0 |
F1@20 | 0.002 9 | 0.024 8 | 0.032 3 | 0.034 4 | 0.024 8 | 0.037 3 | 0.022 8 | 0.049 9 |
NDCG@5 | 0.001 7 | 0.021 2 | 0.032 4 | 0.036 0 | 0.028 9 | 0.036 6 | 0.041 4 | 0.057 8 |
NDCG@10 | 0.001 5 | 0.019 6 | 0.028 5 | 0.031 3 | 0.024 3 | 0.031 9 | 0.034 6 | 0.048 6 |
NDCG@15 | 0.001 3 | 0.018 8 | 0.026 1 | 0.028 4 | 0.021 7 | 0.028 7 | 0.031 0 | 0.043 3 |
NDCG@20 | 0.001 3 | 0.018 2 | 0.024 4 | 0.026 7 | 0.019 9 | 0.026 7 | 0.028 3 | 0.039 7 |
mAP@5 | 0.000 5 | 0.008 9 | 0.014 1 | 0.015 7 | 0.010 0 | 0.014 4 | 0.015 4 | 0.023 7 |
mAP@10 | 0.000 6 | 0.011 2 | 0.016 9 | 0.018 6 | 0.011 7 | 0.017 1 | 0.017 8 | 0.027 7 |
mAP@15 | 0.000 6 | 0.012 6 | 0.018 5 | 0.020 0 | 0.012 6 | 0.018 3 | 0.019 1 | 0.029 7 |
mAP@20 | 0.000 7 | 0.013 6 | 0.019 4 | 0.021 1 | 0.013 2 | 0.019 3 | 0.019 9 | 0.031 1 |
指标 | PMF | MGM | LGLMF | FGCRec | BPR | LMF | SAE-NAD | GWBPR⁃LMF |
---|---|---|---|---|---|---|---|---|
Precision@5 | 0.002 2 | 0.023 8 | 0.040 2 | 0.043 3 | 0.031 1 | 0.046 6 | 0.062 7 | 0.066 1 |
Precision@10 | 0.002 4 | 0.022 9 | 0.035 7 | 0.037 7 | 0.025 2 | 0.038 9 | 0.052 9 | 0.054 5 |
Precision@15 | 0.002 8 | 0.021 5 | 0.032 8 | 0.033 5 | 0.021 4 | 0.035 4 | 0.047 0 | 0.048 3 |
Precision@20 | 0.002 8 | 0.020 9 | 0.030 5 | 0.031 1 | 0.018 8 | 0.032 9 | 0.043 1 | 0.043 5 |
Recall@5 | 0.000 5 | 0.012 7 | 0.020 5 | 0.022 6 | 0.013 9 | 0.024 1 | 0.030 6 | 0.033 6 |
Recall@10 | 0.001 9 | 0.024 2 | 0.035 1 | 0.039 1 | 0.022 4 | 0.039 5 | 0.051 0 | 0.053 9 |
Recall@15 | 0.003 3 | 0.033 3 | 0.048 1 | 0.051 6 | 0.028 6 | 0.052 9 | 0.066 9 | 0.071 1 |
Recall@20 | 0.004 6 | 0.042 5 | 0.058 5 | 0.063 2 | 0.033 2 | 0.064 4 | 0.080 9 | 0.084 1 |
F1@5 | 0.000 8 | 0.016 5 | 0.027 1 | 0.029 6 | 0.019 2 | 0.031 7 | 0.041 1 | 0.044 5 |
F1@10 | 0.002 1 | 0.023 5 | 0.035 3 | 0.038 3 | 0.023 7 | 0.039 1 | 0.051 9 | 0.054 1 |
F1@15 | 0.003 0 | 0.026 1 | 0.039 0 | 0.040 6 | 0.024 4 | 0.042 4 | 0.055 2 | 0.057 5 |
F1@20 | 0.003 4 | 0.028 0 | 0.040 0 | 0.041 6 | 0.024 0 | 0.043 5 | 0.056 2 | 0.057 3 |
NDCG@5 | 0.001 7 | 0.026 3 | 0.040 9 | 0.046 6 | 0.031 3 | 0.048 7 | 0.063 4 | 0.068 0 |
NDCG@10 | 0.001 7 | 0.025 2 | 0.037 6 | 0.041 1 | 0.027 3 | 0.042 6 | 0.057 2 | 0.059 5 |
NDCG@15 | 0.001 6 | 0.024 5 | 0.035 4 | 0.037 6 | 0.024 4 | 0.039 3 | 0.052 5 | 0.054 4 |
NDCG@20 | 0.001 6 | 0.023 7 | 0.033 5 | 0.035 4 | 0.022 2 | 0.036 8 | 0.049 3 | 0.050 4 |
mAP@5 | 0.000 2 | 0.008 0 | 0.011 5 | 0.013 6 | 0.007 3 | 0.013 6 | 0.017 1 | 0.019 9 |
mAP@10 | 0.000 3 | 0.010 5 | 0.014 5 | 0.016 7 | 0.008 9 | 0.016 2 | 0.021 5 | 0.024 0 |
mAP@15 | 0.000 4 | 0.012 1 | 0.016 3 | 0.018 3 | 0.009 5 | 0.017 9 | 0.023 9 | 0.026 4 |
mAP@20 | 0.000 4 | 0.013 4 | 0.017 5 | 0.019 5 | 0.010 0 | 0.018 9 | 0.025 6 | 0.027 8 |
Tab. 4 Experimental results on Gowalla dataset
指标 | PMF | MGM | LGLMF | FGCRec | BPR | LMF | SAE-NAD | GWBPR⁃LMF |
---|---|---|---|---|---|---|---|---|
Precision@5 | 0.002 2 | 0.023 8 | 0.040 2 | 0.043 3 | 0.031 1 | 0.046 6 | 0.062 7 | 0.066 1 |
Precision@10 | 0.002 4 | 0.022 9 | 0.035 7 | 0.037 7 | 0.025 2 | 0.038 9 | 0.052 9 | 0.054 5 |
Precision@15 | 0.002 8 | 0.021 5 | 0.032 8 | 0.033 5 | 0.021 4 | 0.035 4 | 0.047 0 | 0.048 3 |
Precision@20 | 0.002 8 | 0.020 9 | 0.030 5 | 0.031 1 | 0.018 8 | 0.032 9 | 0.043 1 | 0.043 5 |
Recall@5 | 0.000 5 | 0.012 7 | 0.020 5 | 0.022 6 | 0.013 9 | 0.024 1 | 0.030 6 | 0.033 6 |
Recall@10 | 0.001 9 | 0.024 2 | 0.035 1 | 0.039 1 | 0.022 4 | 0.039 5 | 0.051 0 | 0.053 9 |
Recall@15 | 0.003 3 | 0.033 3 | 0.048 1 | 0.051 6 | 0.028 6 | 0.052 9 | 0.066 9 | 0.071 1 |
Recall@20 | 0.004 6 | 0.042 5 | 0.058 5 | 0.063 2 | 0.033 2 | 0.064 4 | 0.080 9 | 0.084 1 |
F1@5 | 0.000 8 | 0.016 5 | 0.027 1 | 0.029 6 | 0.019 2 | 0.031 7 | 0.041 1 | 0.044 5 |
F1@10 | 0.002 1 | 0.023 5 | 0.035 3 | 0.038 3 | 0.023 7 | 0.039 1 | 0.051 9 | 0.054 1 |
F1@15 | 0.003 0 | 0.026 1 | 0.039 0 | 0.040 6 | 0.024 4 | 0.042 4 | 0.055 2 | 0.057 5 |
F1@20 | 0.003 4 | 0.028 0 | 0.040 0 | 0.041 6 | 0.024 0 | 0.043 5 | 0.056 2 | 0.057 3 |
NDCG@5 | 0.001 7 | 0.026 3 | 0.040 9 | 0.046 6 | 0.031 3 | 0.048 7 | 0.063 4 | 0.068 0 |
NDCG@10 | 0.001 7 | 0.025 2 | 0.037 6 | 0.041 1 | 0.027 3 | 0.042 6 | 0.057 2 | 0.059 5 |
NDCG@15 | 0.001 6 | 0.024 5 | 0.035 4 | 0.037 6 | 0.024 4 | 0.039 3 | 0.052 5 | 0.054 4 |
NDCG@20 | 0.001 6 | 0.023 7 | 0.033 5 | 0.035 4 | 0.022 2 | 0.036 8 | 0.049 3 | 0.050 4 |
mAP@5 | 0.000 2 | 0.008 0 | 0.011 5 | 0.013 6 | 0.007 3 | 0.013 6 | 0.017 1 | 0.019 9 |
mAP@10 | 0.000 3 | 0.010 5 | 0.014 5 | 0.016 7 | 0.008 9 | 0.016 2 | 0.021 5 | 0.024 0 |
mAP@15 | 0.000 4 | 0.012 1 | 0.016 3 | 0.018 3 | 0.009 5 | 0.017 9 | 0.023 9 | 0.026 4 |
mAP@20 | 0.000 4 | 0.013 4 | 0.017 5 | 0.019 5 | 0.010 0 | 0.018 9 | 0.025 6 | 0.027 8 |
指标 | Foursquare数据集 | Gowalla数据集 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
BPR | LMF | GWBPR | BPR-LMF | GWBPR-LMF | BPR | LMF | GWBPR | BPR-LMF | GWBPR-LMF | |
Precision@5 | 0.029 3 | 0.039 4 | 0.050 7 | 0.048 0 | 0.0569 | 0.031 1 | 0.046 6 | 0.051 6 | 0.060 3 | 0.066 1 |
Precision@10 | 0.022 0 | 0.031 6 | 0.038 5 | 0.037 1 | 0.0442 | 0.025 2 | 0.038 9 | 0.040 4 | 0.052 9 | 0.054 5 |
Precision@15 | 0.019 0 | 0.027 5 | 0.032 6 | 0.032 0 | 0.0377 | 0.021 4 | 0.035 4 | 0.036 2 | 0.047 0 | 0.048 3 |
Precision@20 | 0.016 9 | 0.025 0 | 0.029 2 | 0.028 5 | 0.0334 | 0.018 8 | 0.032 9 | 0.033 0 | 0.043 4 | 0.043 5 |
Recall@5 | 0.019 7 | 0.028 8 | 0.036 6 | 0.035 2 | 0.0423 | 0.013 9 | 0.024 1 | 0.024 4 | 0.030 5 | 0.033 6 |
Recall@10 | 0.030 0 | 0.046 8 | 0.055 0 | 0.054 3 | 0.0657 | 0.022 4 | 0.039 5 | 0.038 2 | 0.048 1 | 0.053 9 |
Recall@15 | 0.039 3 | 0.061 3 | 0.070 1 | 0.070 4 | 0.0839 | 0.028 6 | 0.052 9 | 0.050 2 | 0.061 1 | 0.071 1 |
Recall@20 | 0.046 8 | 0.073 7 | 0.083 0 | 0.082 7 | 0.0990 | 0.033 2 | 0.064 4 | 0.060 8 | 0.072 9 | 0.084 1 |
F1@5 | 0.023 5 | 0.033 2 | 0.042 5 | 0.040 6 | 0.0485 | 0.019 2 | 0.031 7 | 0.033 1 | 0.040 5 | 0.044 5 |
F1@10 | 0.025 3 | 0.037 7 | 0.045 2 | 0.044 0 | 0.0528 | 0.023 7 | 0.039 1 | 0.039 2 | 0.050 3 | 0.054 1 |
F1@15 | 0.025 6 | 0.037 9 | 0.044 5 | 0.044 0 | 0.0520 | 0.024 4 | 0.042 4 | 0.042 0 | 0.053 1 | 0.057 5 |
F1@20 | 0.024 8 | 0.037 3 | 0.043 2 | 0.042 9 | 0.0499 | 0.024 0 | 0.043 5 | 0.042 7 | 0.054 4 | 0.057 3 |
NDCG@5 | 0.028 9 | 0.036 6 | 0.054 0 | 0.049 5 | 0.0578 | 0.031 3 | 0.048 7 | 0.055 3 | 0.058 3 | 0.068 0 |
NDCG@10 | 0.024 3 | 0.031 9 | 0.045 0 | 0.042 1 | 0.0486 | 0.027 3 | 0.042 6 | 0.046 4 | 0.050 2 | 0.059 5 |
NDCG@15 | 0.021 7 | 0.028 7 | 0.039 8 | 0.038 0 | 0.0433 | 0.024 4 | 0.039 3 | 0.042 0 | 0.045 6 | 0.054 4 |
NDCG@20 | 0.019 9 | 0.026 7 | 0.036 6 | 0.034 8 | 0.0397 | 0.022 2 | 0.036 8 | 0.038 9 | 0.042 6 | 0.050 4 |
mAP@5 | 0.010 0 | 0.014 4 | 0.021 0 | 0.019 1 | 0.0237 | 0.007 3 | 0.013 6 | 0.014 8 | 0.016 8 | 0.019 9 |
mAP@10 | 0.011 7 | 0.017 1 | 0.024 6 | 0.022 5 | 0.0277 | 0.008 9 | 0.016 2 | 0.017 1 | 0.019 8 | 0.024 0 |
mAP@15 | 0.012 6 | 0.018 3 | 0.026 3 | 0.024 3 | 0.0297 | 0.009 5 | 0.017 9 | 0.018 5 | 0.021 4 | 0.026 4 |
mAP@20 | 0.013 2 | 0.019 3 | 0.027 5 | 0.025 4 | 0.0311 | 0.010 0 | 0.018 9 | 0.019 6 | 0.022 6 | 0.027 8 |
Tab. 5 Results of ablation experiments on Foursquare dataset and Gowalla dataset
指标 | Foursquare数据集 | Gowalla数据集 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
BPR | LMF | GWBPR | BPR-LMF | GWBPR-LMF | BPR | LMF | GWBPR | BPR-LMF | GWBPR-LMF | |
Precision@5 | 0.029 3 | 0.039 4 | 0.050 7 | 0.048 0 | 0.0569 | 0.031 1 | 0.046 6 | 0.051 6 | 0.060 3 | 0.066 1 |
Precision@10 | 0.022 0 | 0.031 6 | 0.038 5 | 0.037 1 | 0.0442 | 0.025 2 | 0.038 9 | 0.040 4 | 0.052 9 | 0.054 5 |
Precision@15 | 0.019 0 | 0.027 5 | 0.032 6 | 0.032 0 | 0.0377 | 0.021 4 | 0.035 4 | 0.036 2 | 0.047 0 | 0.048 3 |
Precision@20 | 0.016 9 | 0.025 0 | 0.029 2 | 0.028 5 | 0.0334 | 0.018 8 | 0.032 9 | 0.033 0 | 0.043 4 | 0.043 5 |
Recall@5 | 0.019 7 | 0.028 8 | 0.036 6 | 0.035 2 | 0.0423 | 0.013 9 | 0.024 1 | 0.024 4 | 0.030 5 | 0.033 6 |
Recall@10 | 0.030 0 | 0.046 8 | 0.055 0 | 0.054 3 | 0.0657 | 0.022 4 | 0.039 5 | 0.038 2 | 0.048 1 | 0.053 9 |
Recall@15 | 0.039 3 | 0.061 3 | 0.070 1 | 0.070 4 | 0.0839 | 0.028 6 | 0.052 9 | 0.050 2 | 0.061 1 | 0.071 1 |
Recall@20 | 0.046 8 | 0.073 7 | 0.083 0 | 0.082 7 | 0.0990 | 0.033 2 | 0.064 4 | 0.060 8 | 0.072 9 | 0.084 1 |
F1@5 | 0.023 5 | 0.033 2 | 0.042 5 | 0.040 6 | 0.0485 | 0.019 2 | 0.031 7 | 0.033 1 | 0.040 5 | 0.044 5 |
F1@10 | 0.025 3 | 0.037 7 | 0.045 2 | 0.044 0 | 0.0528 | 0.023 7 | 0.039 1 | 0.039 2 | 0.050 3 | 0.054 1 |
F1@15 | 0.025 6 | 0.037 9 | 0.044 5 | 0.044 0 | 0.0520 | 0.024 4 | 0.042 4 | 0.042 0 | 0.053 1 | 0.057 5 |
F1@20 | 0.024 8 | 0.037 3 | 0.043 2 | 0.042 9 | 0.0499 | 0.024 0 | 0.043 5 | 0.042 7 | 0.054 4 | 0.057 3 |
NDCG@5 | 0.028 9 | 0.036 6 | 0.054 0 | 0.049 5 | 0.0578 | 0.031 3 | 0.048 7 | 0.055 3 | 0.058 3 | 0.068 0 |
NDCG@10 | 0.024 3 | 0.031 9 | 0.045 0 | 0.042 1 | 0.0486 | 0.027 3 | 0.042 6 | 0.046 4 | 0.050 2 | 0.059 5 |
NDCG@15 | 0.021 7 | 0.028 7 | 0.039 8 | 0.038 0 | 0.0433 | 0.024 4 | 0.039 3 | 0.042 0 | 0.045 6 | 0.054 4 |
NDCG@20 | 0.019 9 | 0.026 7 | 0.036 6 | 0.034 8 | 0.0397 | 0.022 2 | 0.036 8 | 0.038 9 | 0.042 6 | 0.050 4 |
mAP@5 | 0.010 0 | 0.014 4 | 0.021 0 | 0.019 1 | 0.0237 | 0.007 3 | 0.013 6 | 0.014 8 | 0.016 8 | 0.019 9 |
mAP@10 | 0.011 7 | 0.017 1 | 0.024 6 | 0.022 5 | 0.0277 | 0.008 9 | 0.016 2 | 0.017 1 | 0.019 8 | 0.024 0 |
mAP@15 | 0.012 6 | 0.018 3 | 0.026 3 | 0.024 3 | 0.0297 | 0.009 5 | 0.017 9 | 0.018 5 | 0.021 4 | 0.026 4 |
mAP@20 | 0.013 2 | 0.019 3 | 0.027 5 | 0.025 4 | 0.0311 | 0.010 0 | 0.018 9 | 0.019 6 | 0.022 6 | 0.027 8 |
1 | RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]// Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. Arlington, VA: AUAI Press, 2015: 452-461. 10.1609/aaai.v30i1.9973 |
2 | LI X T, CONG G, LI X L, et al. Rank-GeoFM: a ranking based geographical factorization method for point of interest recommendation[C]// Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2015: 433-442. 10.1145/2766462.2767722 |
3 | CHENG C, YANG H Q, KING I, et al. A unified point-of-interest recommendation framework in location-based social networks[J]. ACM Transactions on Intelligent Systems and Technology, 2017, 8(1): No.10. 10.1145/2901299 |
4 | 张松慧,熊汉江. 融合神经网络和泊松分解的兴趣点推荐算法[J]. 计算机工程与应用, 2020, 56(21):176-186. |
ZHANG S H, XIONG H J. Point-of-interest recommendation algorithm based on Poisson factorization and neural network[J]. Computer Engineering and Applications, 2020, 56(21): 176-186. | |
5 | KARATZOGLOU A, BALTRUNAS L, SHI Y. Learning to rank for recommender systems[C]// Proceedings of the 7th ACM Conference on Recommender Systems. New York: ACM, 2013: 493-494. 10.1145/2507157.2508063 |
6 | CHENG C, YANG H Q, KING I, et al. Fused matrix factorization with geographical and social influence in location-based social networks[C]// Proceedings of the 26th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2012: 17-23. |
7 | YE M, YIN P F, LEE W C, et al. Exploiting geographical influence for collaborative point-of-interest recommendation[C]// Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2011: 325-334. 10.1145/2009916.2009962 |
8 | ZHANG J D, CHOW C Y. iGSLR: personalized geo-social location recommendation: a kernel density estimation approach[C]// Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: ACM, 2013: 334-343. 10.1145/2525314.2525339 |
9 | LIAN D F, ZHAO C, XIE X, et al. GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2014: 831-840. 10.1145/2623330.2623638 |
10 | WANG X L, SUN G H, FANG X, et al. Modeling spatio-temporal neighbourhood for personalized point-of-interest recommendation[C]// Proceedings of the 31st International Joint Conference on Artificial Intelligence. California: ijcai.org, 2022: 3530-3536. 10.24963/ijcai.2022/490 |
11 | 安敬民,李冠宇,蒋伟,等. 基于用户活动轨迹和个性化区域划分的兴趣点推荐[J]. 计算机学报, 2022, 45(6):1176-1194. 10.11897/SP.J.1016.2022.01176 |
AN J M, LI G Y, JIANG W, et al. A point-of-interest recommendation method based on activity tracks and personalized-area partitions of users[J]. Chinese Journal of Computers, 2022, 45(6): 1176-1194. 10.11897/SP.J.1016.2022.01176 | |
12 | YUAN Q, CONG G, MA Z Y, et al. Time-aware point-of-interest recommendation[C]// Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013: 363-372. 10.1145/2484028.2484030 |
13 | RAHMANI H A, ALIANNEJADI M, BARATCHI M, et al. Joint geographical and temporal modeling based on matrix factorization for point-of-interest recommendation[C]// Proceedings of the 2020 European Conference on Information Retrieval, LNCS 12035. Cham: Springer, 2020: 205-219. |
14 | 李寒露,解庆,唐伶俐,等. 融合时空信息和兴趣点重要性的POI推荐算法[J]. 计算机应用, 2020, 40(9):2600-2605. |
LI H L, XIE Q, TANG L L, et al. POI recommendation algorithm combining spatiotemporal information and POI importance[J]. Journal of Computer Applications, 2020, 40(9): 2600-2605. | |
15 | 王营丽,姜聪聪,冯小年,等. 时间感知的兴趣点推荐方法[J]. 计算机科学, 2021, 48(9):43-49. 10.11896/jsjkx.210400130 |
WANG Y L, JIANG C C, FENG X N, et al. Time aware point-of-interest recommendation[J]. Computer Science, 2021, 48(9): 43-49. 10.11896/jsjkx.210400130 | |
16 | ZHANG J D, CHOW C Y. GeoSoCa: exploiting geographical, social and categorical correlations for point-of-interest recommendations[C]// Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2015: 443-452. 10.1145/2766462.2767711 |
17 | RAHMANI H A, ALIANNEJADI M, MIRZAEI ZADEH R, et al. Category-aware location embedding for point-of-interest recommendation[C]// Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval. New York: ACM, 2019: 173-176. 10.1145/3341981.3344240 |
18 | 董婵娟,李胜,何熊熊,等. 融合地理信息、种类信息与隐式社交关系的兴趣点推荐算法[J]. 模式识别与人工智能, 2021, 34(2):106-116. |
DONG C J, LI S, HE X X, et al. Point of interest recommendation algorithm integrating geo-category information and implicit social relationship[J]. Pattern Recognition and Artificial Intelligence, 2021, 34(2): 106-116. | |
19 | GUO L, JIANG H R, WANG X H, et al. Learning to recommend point-of-interest with the weighted Bayesian personalized ranking method in LBSNs[J]. Information, 2017, 8(1): No.20. 10.3390/info8010020 |
20 | YUAN F J, JOSE J M, GUO G B, et al. Joint geo-spatial preference and pairwise ranking for point-of-interest recommendation[C]// Proceedings of the IEEE 28th International Conference on Tools with Artificial Intelligence. Piscataway: IEEE, 2016: 46-53. 10.1109/ictai.2016.0018 |
21 | LIU B P, SU Y J, ZHA D R, et al. CARec: content-aware point-of-interest recommendation via adaptive Bayesian personalized ranking[J]. Australian Journal of Intelligent Information Processing Systems, 2019, 15(3): 61-68. |
22 | MANOTUMRUKSA J, MACDONALD C, OUNIS I. A personalised ranking framework with multiple sampling criteria for venue recommendation[C]// Proceedings of the 2017 ACM Conference on Information and Knowledge Management. New York: ACM, 2017: 1469-1478. 10.1145/3132847.3132985 |
23 | JOHNSON C C. Logistic matrix factorization for implicit feedback data[EB/OL]. [2022-03-20].. |
24 | LIU Y D, PHAM T A N, CONG G, et al. An experimental evaluation of point-of-interest recommendation in location-based social networks[J]. Proceedings of the VLDB Endowment, 2017, 10(10): 1010-1021. 10.14778/3115404.3115407 |
25 | GAO R, LI J, DU B, et al. Exploiting geo-social correlations to improve pairwise ranking for point-of-interest recommendation[J]. China Communications, 2018, 15(7): 180-201. 10.1109/cc.2018.8424613 |
26 | SALAKHUTDINOV R, MNIH A. Probabilistic matrix factorization[C]// Proceedings of the 20th International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2007: 1257-1264. 10.1145/1390156.1390267 |
27 | RAHMANI H A, ALIANNEJADI M, AHMADIAN S, et al. LGLMF: local geographical based logistic matrix factorization model for POI recommendation[C]// Proceedings of the 2019 Asia Information Retrieval Symposium, LNCS 12004. Cham: Springer, 2020: 66-78. |
28 | SU Y J, LI X, LIU B P, et al. FGCRec: fine-grained geographical characteristics modeling for point-of-interest recommendation[C]// Proceedings of the 2020 IEEE International Conference on Communications. Piscataway: IEEE, 2020: 1-6. 10.1109/icc40277.2020.9148797 |
29 | MA C, ZHANG Y X, WANG Q L, et al. Point-of-interest recommendation: exploiting self-attentive autoencoders with neighbor-aware influence[C]// Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York: ACM, 2018: 697-706. 10.1145/3269206.3271733 |
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