基于卷积神经网络的评论文本兴趣点推荐算法
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金(11971277); 山西省教育科学“十三五”规划项目(GH-18045); 山西大同大学校级科研项目(2020YGZX016); 山西大同大学校级科研项目(2020K10)


Point of Interest Recommendation Algorithm of Review Text Based on CNN
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 增强出版
  • |
  • 文章评论
    摘要:

    针对基于位置社交网络中的兴趣点推荐存在用户签到数据稀疏、评论文本信息利用不充分、推荐准确度不高等问题, 提出一种基于卷积神经网络的评论文本兴趣点推荐模型(RT-CNN). 首先采用高斯函数利用邻近地理位置加权方法填补矩阵分解模型中缺少的位置信息, 预测用户对未签到位置的潜在兴趣. 然后通过卷积神经网络处理评论文本信息挖掘潜在特征, 深度提取用户情感倾向, 使用Softmax逻辑回归函数获得评论文本与用户和位置兴趣点潜在特征相关的概率, 通过对目标函数的求解提取用户和位置潜在特征向量. 最后融合签到行为、地理位置影响、用户情感倾向、用户潜在特征和位置兴趣点潜在特征进行兴趣点推荐. 在公开的Foursquare网站纽约(NYC)和洛杉矶(LA)两个真实签到数据集进行实验, 结果表明RT-CNN模型相比其他先进的兴趣点推荐模型提高了精确率和召回率, 具有更好的推荐性能.

    Abstract:

    In view of the sparse user check-in data, underutilization of review text information, and low accuracy of point-of-interest recommendation in location-based social networks, this study proposes a point-of-interest recommendation model based on review texts and the convolutional neural network (CNN), or an RT-CNN point-of-interest recommendation model for short. To start with, the Gaussian function and adjacent geographical location weighting are used to fill in the missing location information in the matrix decomposition model and thereby predict the user’s potential interest in unchecked locations. Then, review text information is processed by the convolutional neural network to mine potential features and ultimately to extract the user’s emotional tendencies in depth. The Softmax logic regression function is utilized to obtain the probabilities of the review text related to the potential features of a user and a point-of-interest location, and potential feature vectors of the user and the location are extracted by solving the objective function. Finally, the check-in behavior, geographical location influence, user emotion tendencies, user potential features, and potential features of point-of-interest locations are integrated to recommend points-of-interest. Experiments are carried out on two real check-in datasets, namely NYC and LA, on the public website Foursquare. The results show that compared with other state-of-the-art point-of-interest recommendation models, the RT-CNN model improves the accuracy rate and the recall rate and has better recommendation performance.

    参考文献
    相似文献
    引证文献
引用本文

申晋祥,鲍美英.基于卷积神经网络的评论文本兴趣点推荐算法.计算机系统应用,,():1-5

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-11-01
  • 最后修改日期:2021-12-02
  • 录用日期:
  • 在线发布日期: 2022-05-30
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号