Affiliation:
1. School of Management, Xi’an University of Science and Technology, Xi’an, Shaanxi 710054, China
Abstract
To solve the problems of large data sparsity and lack of negative samples in most point of interest (POI) recommendation methods, a POI recommendation method based on deep learning in location-based social networks is proposed. Firstly, a bidirectional long-short-term memory (Bi-LSTM) attention mechanism is designed to give different weights to different parts of the current sequence according to users’ long-term and short-term preferences. Then, the POI recommendation model is constructed, the sequence state data of the encoder is input into Bi-LSTM-Attention to get the attention representation of the current POI check-in sequence, and the Top-
recommendation list is generated after the decoder processing. Finally, a negative sampling method is proposed to obtain an effective negative sample set, which is used to improve the calculation of the Bayesian personalized ranking loss function. The proposed method is demonstrated experimentally on Foursquare and Gowalla datasets. The experimental results show that the proposed method has better accuracy, recall, and F1 value than other comparison methods.
Funder
Education Department of Shaanxi Province
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献