Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos. (15th March 2018)

• Record Type: Journal Article
• Authors: Cai, Guochen; Lee, Kyungmi; Lee, Ickjai
• Is Part Of: Expert systems with applications. Volume 94(2018)
• Page Start: 32
• Page End: 40
• Publication Date: 2018-03-15
• Subjects: Semantics -- Recommender systems -- Geotagged photos -- Trajectory pattern mining; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.10.049
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Propose a semantic-level itinerary recommender system from geo-tagged photos. Incorporation of semantic trajectory patterns into itinerary recommendations. Overcome the inefficiency of traditional itinerary recommender systems. Experimental results to demonstrate the effectiveness of proposed framework. Abstract: 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.