计算机科学
偏爱
期限(时间)
循环神经网络
推荐系统
点(几何)
任务(项目管理)
人工智能
兴趣点
情报检索
序列(生物学)
机器学习
数据挖掘
人工神经网络
工程类
物理
生物
量子力学
遗传学
经济
微观经济学
系统工程
数学
几何学
作者
Ke Sun,Tieyun Qian,Tong Chen,Yile Liang,Quoc Viet Hung Nguyen,Hongzhi Yin
出处
期刊:Proceedings of the ... AAAI Conference on Artificial Intelligence
[Association for the Advancement of Artificial Intelligence (AAAI)]
日期:2020-04-03
卷期号:34 (01): 214-221
被引量:247
标识
DOI:10.1609/aaai.v34i01.5353
摘要
Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.
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