基于时序和距离的门控循环单元兴趣点推荐算法

doi:10.19678/j.issn.1000-3428.0053659

计算机工程 ›› 2020, Vol. 46 ›› Issue (1): 52-59. doi: 10.19678/j.issn.1000-3428.0053659

基于时序和距离的门控循环单元兴趣点推荐算法

夏永生¹, 王晓蕊², 白鹏¹, 李梦梦¹, 夏阳¹, 张凯¹

1. 中国矿业大学计算机科学与技术学院, 江苏徐州 221116;
2. 苏宁易购集团股份有限公司, 南京 210042

收稿日期:2019-01-11 修回日期:2019-03-06 出版日期:2020-01-15 发布日期:2019-03-14
作者简介:夏永生(1994-),男,硕士研究生,主研方向为推荐系统、自然语言处理;王晓蕊,硕士;白鹏、李梦梦,硕士研究生;夏阳(通信作者),教授、博士;张凯,硕士研究生。
基金资助:
国家自然科学基金（51874300）；国家自然科学基金委员会-山西省人民政府煤基低碳联合基金（U1510115）；中国博士后科学基金特别项目（2013T60574）。

Point of Interest Recommendation Algorithm of Gated Recurrent Unit Based on Time Series and Distance

XIA Yongsheng¹, WANG Xiaorui², BAI Peng¹, LI Mengmeng¹, XIA Yang¹, ZHANG Kai¹

1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China;
2. Suning E-commerce Group Co., Ltd., Nanjing 210042, China

Received:2019-01-11 Revised:2019-03-06 Online:2020-01-15 Published:2019-03-14

摘要/Abstract

摘要： 兴趣点推荐算法多数易受时间因素与地理位置因素的影响，造成兴趣点的相关文本信息具有不完整性和模糊性。从地理位置与时间相关性出发，提出基于时序和距离的门控循环单元兴趣点推荐算法。利用门控循环单元模型对时间序列和相关距离信息进行建模，提取用户访问兴趣点的偏好特征，并基于该特征对用户进行兴趣点推荐。在真实数据集上进行的实验结果表明，与传统循环神经网络算法相比，该算法能够覆盖用户访问兴趣点的长序列，推荐结果更具可靠性。

Abstract: Most Point of Interest(POI) recommendation algorithms are susceptible to the influence of time and geographical location,causing incompleteness and ambiguity in related text information of POI.Starting from the correlation between time and geographical location,this paper proposes a POI recommendation algorithm of gated recurrent unit based on time series and distance.The model of time series and related distance information are established on the basis of the gated recurrent unit model.The preference feature of user's access POI is extracted and recommendation of user's POI is made according to this feature.Experimental results on real datasets show that compared with the traditional recurrent neural network algorithm,the proposed algorithm can cover long sequences of user's POI,and the recommendation results are more reliable.

Key words: Point of Interest(POI) recommendation, deep learning, gated recurrent unit, geographical location, time series

中图分类号:

TP18

夏永生, 王晓蕊, 白鹏, 李梦梦, 夏阳, 张凯. 基于时序和距离的门控循环单元兴趣点推荐算法[J]. 计算机工程, 2020, 46(1): 52-59.

XIA Yongsheng, WANG Xiaorui, BAI Peng, LI Mengmeng, XIA Yang, ZHANG Kai. Point of Interest Recommendation Algorithm of Gated Recurrent Unit Based on Time Series and Distance[J]. Computer Engineering, 2020, 46(1): 52-59.

http://www.ecice06.com/CN/Y2020/V46/I1/52

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