Personalized Travel Package With Multi-Point-of-Interest Recommendation Based on Crowdsourced User Footprints

• Published in: IEEE transactions on human-machine systems Vol. 46; no. 1; pp. 151 - 158
• Main Authors: Yu, Zhiwen, Xu, Huang, Yang, Zhe, Guo, Bin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Entertainment industry; Filtering; Footprints; Location-based social networks (LBSNs); Man machine systems; Packages; Personalized; Planning; point-of-interest (POI) detection; Poles and towers; Recommendations; Recommender systems; Route planning; Trajectory; travel package recommendation; travel route planning (TRP); Urban areas; point-of-interest (POI) detection; Location-based social networks (LBSNs); travel package recommendation; travel route planning (TRP)
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2015.2446953

Location-based social networks (LBSNs) provide people with an interface to share their locations and write reviews about interesting places of attraction. The shared locations form the crowdsourced digital footprints, in which each user has many connections to many locations, indicating user preference to locations. In this paper, we propose an approach for personalized travel package recommendation to help users make travel plans. The approach utilizes data collected from LBSNs to model users and locations, and it determines users' preferred destinations using collaborative filtering approaches. Recommendations are generated by jointly considering user preference and spatiotemporal constraints. A heuristic search-based travel route planning algorithm was designed to generate travel packages. We developed a prototype system, which obtains users' travel demands from mobile client and generates travel packages containing multiple points of interest and their visiting sequence. Experimental results suggest that the proposed approach shows promise with respect to improving recommendation accuracy and diversity.