[논문]OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services

Nguyen, Luong Vuong; Jung, Jason J.; Hwang, Myunggwon

doi:10.3390/ijgi9120711

[해외논문] OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services 원문보기

ISPRS international journal of geo-information, v.9 no.12, 2020년, pp.711 -

Nguyen, Luong Vuong (Department of Computer Engineering, Chung-Ang University, Seoul 156-756, Korea) , Jung, Jason J. (Department of Computer Engineering, Chung-Ang University, Seoul 156-756, Korea) , Hwang, Myunggwon (Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 136-791, Korea)

Abstract ▼ AI-Helper

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.

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