融合时空信息和兴趣点重要性的POI推荐算法

doi:10.11772/j.issn.1001-9081.2020010060

计算机应用 ›› 2020, Vol. 40 ›› Issue (9): 2600-2605.DOI: 10.11772/j.issn.1001-9081.2020010060

融合时空信息和兴趣点重要性的POI推荐算法

李寒露, 解庆, 唐伶俐, 刘永坚

武汉理工大学计算机科学与技术学院, 武汉 430070

收稿日期:2020-01-21 修回日期:2020-04-19 出版日期:2020-09-10 发布日期:2020-04-28
通讯作者: 解庆
作者简介:李寒露(1995-),女,湖北襄阳人,硕士研究生,主要研究方向:推荐系统;解庆(1986-),男,湖北武汉人,副教授,博士,CCF会员,主要研究方向:数据挖掘、机器学习;唐伶俐(1989-),女,湖北荆州人,讲师,博士,主要研究方向:数字传播工程、版权保护;刘永坚(1962-),男,湖北武汉人,教授,主要研究方向:出版融合、知识服务。
基金资助:
国家自然科学基金资助项目（61602353）。

POI recommendation algorithm combining spatiotemporal information and POI importance

LI Hanlu, XIE Qing, TANG Lingli, LIU Yongjian

School of Computer Science and Technology, Wuhan University of Technology, Wuhan Hubei 430070, China

Received:2020-01-21 Revised:2020-04-19 Online:2020-09-10 Published:2020-04-28
Supported by:
This work is partially supported by the National Natural Foundation of China (61602353).

摘要/Abstract

摘要： 针对兴趣点（POI）推荐研究中数据噪声过滤问题和不同POI的重要性问题，提出了一种融合时空信息和兴趣点重要性的POI推荐算法——RecSI。首先，根据POI的地理信息和POI之间相互吸引力过滤噪声数据，缩小候选集的范围；其次，根据用户在一天中不同的时间段对POI类别的偏好程度，结合POI的流行度计算出用户的偏好得分；然后，结合社交信息和加权PageRank算法计算POI重要性；最后，将用户的偏好得分和POI重要性线性结合，以向用户推荐TOP-K的POI。在Foursquare真实的签到数据集上的实验结果表明，RecSI算法的精确率和召回率比最优的GCSR算法分别提高了12.5%和6%，验证了RecSI算法的有效性。

关键词: 兴趣点重要性, 时空信息, 兴趣点类别, 社交信息, 数据噪声

Abstract: Aiming at the data noise filtering problem and the importance problem of different POIs in POI (Point-Of-Interest)recommendation research, a POI recommendation algorithm, named RecSI (Recommendation by Spatiotemporal information and POI Importance), was proposed. First, the geographic information and the mutual attraction between the POIs were used to filter out the data noise, so as to narrow the range of candidate set. Second, the user’s preference score was calculated by combining the user’s preference on the POI category at different time periods of the day and the popularities of the POIs. Then, the importances of different POIs were calculated by combining social information and weighted PageRank algorithm. Finally, the user’s preference score and POI importances were linearly combined in order to recommend TOP-K POIs to the user. Experimental results on real Foursquare sign-in dataset show that the precision and recall of the RecSI algorithm are higher than those of baseline GCSR (Geography-Category-Socialsentiment fusion Recommendation) algorithm by 12.5% and 6% respectively, which verify the effectiveness of RecSI algorithm.

Key words: Point-Of-Interest (POI) importance, spatiotemporal information, POI category, social information, data noise

中图分类号:

TP391.3

李寒露, 解庆, 唐伶俐, 刘永坚. 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 Hanlu, XIE Qing, TANG Lingli, LIU Yongjian. POI recommendation algorithm combining spatiotemporal information and POI importance[J]. Journal of Computer Applications, 2020, 40(9): 2600-2605.

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融合时空信息和兴趣点重要性的POI推荐算法

POI recommendation algorithm combining spatiotemporal information and POI importance

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