Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos

被引：77
作者：
Cai, Guochen [1 ]
Lee, Kyungmi [1 ]
Lee, Ickjai [1 ]
机构：
[1] James Cook Univ, Informat Technol Acad, Coll Business Law & Governance, POB 6811, Cairns, Qld 4870, Australia
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2018年 / 94卷
关键词：
Semantics; Recommender systems; Geotagged photos; Trajectory pattern mining; TRAVEL RECOMMENDATION; SOCIAL MEDIA;
D O I：
10.1016/j.eswa.2017.10.049
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A large number of geo-tagged photos become available online due to the advances in geo-tagging services and Web technologies. These geo-tagged photos are indicative of photo-takers' trails and movements, and have been used for mining people movements and trajectory patterns. These geo-tagged photos are inherently spatio-temporal, sequential and implicitly containing aspatial semantics. and recommender systems are collaborative filtering based. There have been some studies to build itinerary recommender systems from these geo-tagged photos, but they fail to consider these dimensions and share some common drawbacks, especially lacking aspatial semantics or temporal information. This paper proposes an itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos by discovering sequential points-of-interest with temporal information from other users' visiting sequences and preferences. Our system considers spatio-temporal, sequential, and aspatial semantics dimensions, and also takes into account user-specified preferences and constraints to customise their requests. It generates a set of customised and targeted semantic-level itineraries meeting the user specified constraints. The proposed method generates these semantic itineraries from historic people's movements by mining frequent travel patterns from geo-tagged photos. Experimental results demonstrate the informativeness, efficiency and effectiveness of our proposed method over traditional approaches. Crown Copyright (C) 2017 Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：32 / 40
页数：9
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