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。
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.
作者
夏永生
王晓蕊
白鹏
李梦梦
夏阳
张凯
XIA Yongsheng;WANG Xiaorui;BAI Peng;LI Mengmeng;XIA Yang;ZHANG Kai(School of Computer Science and Technology,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China;Suning E-commerce Group Co.,Ltd.,Nanjing 210042,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2020年第1期52-59,共8页
Computer Engineering
基金
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)
关键词
兴趣点推荐
深度学习
门控循环单元
地理位置
时间序列
Point of Interest(POI)recommendation
deep learning
gated recurrent unit
geographical location
time series
