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Article

OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services

1
Department of Computer Engineering, Chung-Ang University, Seoul 156-756, Korea
2
Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 136-791, Korea
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(12), 711; https://doi.org/10.3390/ijgi9120711
Received: 14 October 2020 / Revised: 12 November 2020 / Accepted: 13 November 2020 / Published: 27 November 2020
(This article belongs to the Special Issue Intelligent Systems Based on Open and Crowdsourced Location Data)
This paper presents a cross-cultural crowdsourcing platform, called OurPlaces, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (i) places (locations where people have visited); (ii) cognition (how people have experienced these places); and (iii) users (those who have visited these places). Notably, cognition is represented as a paring of two similar places from different cultures (e.g., Versailles and Gyeongbokgung in France and Korea, respectively). As a case study, we applied the OurPlaces platform to a cross-cultural tourism recommendation system and conducted a simulation using a dataset collected from TripAdvisor. The tourist places were classified into four types (i.e., hotels, restaurants, shopping malls, and attractions). In addition, user feedback (e.g., ratings, rankings, and reviews) from various nationalities (assumed to be equivalent to cultures) was exploited to measure the similarities between tourism places and to generate a cognition layer on the platform. To demonstrate the effectiveness of the OurPlaces-based system, we compared it with a Pearson correlation-based system as a baseline. The experimental results show that the proposed system outperforms the baseline by 2.5% and 4.1% in the best case in terms of MAE and RMSE, respectively. View Full-Text
Keywords: recommendation systems; crowdsourcing platform; cognitive similarity; similar places recommendation systems; crowdsourcing platform; cognitive similarity; similar places
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MDPI and ACS Style

Nguyen, L.V.; Jung, J.J.; Hwang, M. OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services. ISPRS Int. J. Geo-Inf. 2020, 9, 711. https://doi.org/10.3390/ijgi9120711

AMA Style

Nguyen LV, Jung JJ, Hwang M. OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services. ISPRS International Journal of Geo-Information. 2020; 9(12):711. https://doi.org/10.3390/ijgi9120711

Chicago/Turabian Style

Nguyen, Luong V., Jason J. Jung, and Myunggwon Hwang. 2020. "OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services" ISPRS International Journal of Geo-Information 9, no. 12: 711. https://doi.org/10.3390/ijgi9120711

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