Recommending Point-of-Interests with Real-Time Event Detection

被引：0
|
作者：
Zhi L. [1 ,2 ]
Rui S. [2 ]
Yuxuan Y. [1 ,3 ]
Xiaohuan L. [2 ]
机构：
[1] School of Information Engineering, Hunan Mechanical and Electrical Polytechnic, Changsha
[2] Modern Applied Statistics and Big Data Research Center, Huaqiao University, Quanzhou
[3] School of Information Science and Engineering, Hunan University, Changsha
来源：
Data Analysis and Knowledge Discovery | 2022年 / 6卷 / 10期
关键词：
Convolutional Neural Network; Deep Learning; Matrix Factorization; Real-Time Event; Recommendation System;
D O I：
10.11925/infotech.2096-3467.2021.1461
中图分类号：
学科分类号：
摘要：
[Objective] This paper constructs a point-of-interest (POI) recommendation system based on real-time event detection, appropriate time and POI characteristics. [Methods] First, we retrieved the real-time events from a large number of tweets with geographical markers. Then, the system learned the embedded feature representation of real-time events and time perception information through tree convolution neural network. Third, we captured the perceptual features of POI’s graphic contents from comments and photos. Fourth, the system learned the graphic feature vector of POI with convolution neural network. Finally, we used the recall rate at the top K and the average of the reciprocal of the ranking to evaluate the effectiveness of different recommendation systems. [Results] The mean reciprocal rank (MRR) of the proposed model is 8.9% higher than that of the MP model and 57.9% higher than that of the non-negative matrix factorization (NMF) model. [Limitations] The characteristics of POI only include textual and image features, which need to be expanded. [Conclusions] The proposed model could effectively recommend point-of-interests, which benefits location-based services such as search, transportation and environmental monitoring. © 2022, Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：114 / 127
页数：13
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