Next location recommendation based on spatiotemporal-aware GRU and attention

doi:10.11772/j.issn.1001-9081.2019071289

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (3): 677-682.DOI: 10.11772/j.issn.1001-9081.2019071289

• Artificial intelligence • Previous Articles Next Articles

Next location recommendation based on spatiotemporal-aware GRU and attention

LI Quan, XU Xinhua, LIU Xinghong, CHEN Qi

School of Computer and Information Engineering, Hubei Normal University, Huangshi Hubei 435002, China

Received:2019-07-25 Revised:2019-10-01 Online:2020-03-10 Published:2019-10-25
Supported by:
This work is partially supported by the Science and Technology Project of Education Department of Hubei Province (B2018150), the Science and Technology Program of State Archives Bureau (2016-x-51), the Scientific and Technological Innovation Team Program of Excellent Middle-aged and Young People in Colleges in Hubei Province (T201515).

融合时空感知GRU和注意力的下一个地点推荐

李全, 许新华, 刘兴红, 陈琦

湖北师范大学计算机与信息工程学院, 湖北黄石 435002

通讯作者: 李全
作者简介:李全(1982-),男,湖北黄石人,讲师,硕士,主要研究方向:机器学习、数据挖掘;许新华(1968-),男,湖北孝感人,教授,硕士,主要研究方向:数据库、数据挖掘;刘兴红(1969-),女,湖北蕲春人,教授,硕士,主要研究方向:大数据、教育信息化;陈琦(1980-),女,辽宁丹东人,副教授,博士,主要研究方向:机器学习、计算机视觉。
基金资助:
湖北省教育厅科技项目（B2018150）；国家档案局科技计划项目（2016-x-51）；湖北省高等学校优秀中青年科技创新团队计划项目（T201515）。

Abstract

Abstract: Aiming at the problem that the influence of time and space information of the location was not considered when making the location recommendation by Gated Recurrent Unit (GRU) of recurrent neural network, the spatiotemporal-aware GRU model was proposed. In addition, aiming at the noise problem generated by the unrelated check-in data in check-in sequence, the next location recommendation method of SpatioTemporal-aware GRU and Attention (ST-GRU+Attention) was proposed. Firstly, time gate and distance gate were added in the GRU model by counting the time slot and distance gap between two locations. The influence of time and space information on recommending next location was controlled by setting the weight matrices. Secondly, the attention mechanism was introduced. The attention weight coefficients of the user were obtained by calculating the attention weight scores of the user preferences, and the personalized preference of the user was obtained. Finally, the objective function was constructed and the model parameters were learned by Bayesian Personalized Ranking (BPR) algorithm. The experimental results show that the accuracy of ST-GRU+Attention is improved significantly compared to the recommendation methods of Factorizing Personalized Markov Chain and Localized Region (FPMC-LR), Personalized Ranking Metric Embedding (PRME) and Spatial Temporal Recurrent Neural Network (ST-RNN), and the precision and recall of ST-GRU+Attention are increased by 15.4% and 17.1% respectively compared to those of ST-RNN which is the best of the three methods. The recommendation method of ST-GRU+Attention can effectively improve the effect of next location recommendation.

Key words: Recurrent Neural Network (RNN), location recommendation, personalized preference, attention mechanism, Bayesian Personalized Ranking (BPR) sorting

摘要： 针对循环神经网络（RNN）的门控循环单元（GRU）在进行地点推荐时没有考虑地点的时间和空间信息的影响，提出了融合时空感知的GRU模型。另外，对于签到序列中不相关的签到数据会产生噪声的问题，提出了融合时空感知的GRU和注意力的下一个地点推荐模型（ST-GRU+Attention）。首先，通过计算两个地点之间时间间隙和距离间隙，在GRU模型的基础上增加时间门和空间门，设置权重矩阵，控制时间信息和空间信息对推荐下一个地点的影响；然后，引入注意力机制，通过计算用户偏好的注意力权重得分，得到用户的注意力权重系数，获取用户的个性化偏好；最后，通过贝叶斯个性化排序（BPR）算法构造目标函数并学习模型参数。实验结果表明，与个性化马尔可夫链和用户位置受限的推荐方法（FPMC-LR）、基于个性化排名度量嵌入的推荐方法（PRME）和融合时间和空间的循环神经网络（ST-RNN）的推荐方法相比，ST-GRU+Attention的准确度有了较大的提高，其准确率（Precision）和召回率（Recall）两项指标比较优的ST-RNN算法分别提高了15.4%和17.1%。ST-GRU+Attention推荐方法可以有效地改善地点推荐的结果。

CLC Number:

TP301

LI Quan, XU Xinhua, LIU Xinghong, CHEN Qi. Next location recommendation based on spatiotemporal-aware GRU and attention[J]. Journal of Computer Applications, 2020, 40(3): 677-682.

李全, 许新华, 刘兴红, 陈琦. 融合时空感知GRU和注意力的下一个地点推荐[J]. 计算机应用, 2020, 40(3): 677-682.

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Next location recommendation based on spatiotemporal-aware GRU and attention

融合时空感知GRU和注意力的下一个地点推荐

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