<i>OurPlaces</i>: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services
This paper presents a cross-cultural crowdsourcing platform, called <i>OurPlaces</i>, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (<i>i</i>) places (locations where people have visited); (<inl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/9/12/711 |
| _version_ | 1797546466829402112 |
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| author | Luong Vuong Nguyen Jason J. Jung Myunggwon Hwang |
| author_facet | Luong Vuong Nguyen Jason J. Jung Myunggwon Hwang |
| author_sort | Luong Vuong Nguyen |
| collection | DOAJ |
| description | This paper presents a cross-cultural crowdsourcing platform, called <i>OurPlaces</i>, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (<i>i</i>) places (locations where people have visited); (<inline-formula><math display="inline"><semantics><mrow><mi>i</mi><mi>i</mi></mrow></semantics></math></inline-formula>) cognition (how people have experienced these places); and (<inline-formula><math display="inline"><semantics><mrow><mi>i</mi><mi>i</mi><mi>i</mi></mrow></semantics></math></inline-formula>) 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 <i>OurPlaces</i> 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 <i>OurPlaces</i>-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. |
| first_indexed | 2024-03-10T14:29:55Z |
| format | Article |
| id | doaj.art-7d0597f05d0a43d4a37c4b75b1bffda5 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T14:29:55Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-7d0597f05d0a43d4a37c4b75b1bffda52023-11-20T22:40:59ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-11-0191271110.3390/ijgi9120711<i>OurPlaces</i>: Cross-Cultural Crowdsourcing Platform for Location Recommendation ServicesLuong Vuong Nguyen0Jason J. Jung1Myunggwon Hwang2Department of Computer Engineering, Chung-Ang University, Seoul 156-756, KoreaDepartment of Computer Engineering, Chung-Ang University, Seoul 156-756, KoreaKorea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 136-791, KoreaThis paper presents a cross-cultural crowdsourcing platform, called <i>OurPlaces</i>, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (<i>i</i>) places (locations where people have visited); (<inline-formula><math display="inline"><semantics><mrow><mi>i</mi><mi>i</mi></mrow></semantics></math></inline-formula>) cognition (how people have experienced these places); and (<inline-formula><math display="inline"><semantics><mrow><mi>i</mi><mi>i</mi><mi>i</mi></mrow></semantics></math></inline-formula>) 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 <i>OurPlaces</i> 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 <i>OurPlaces</i>-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.https://www.mdpi.com/2220-9964/9/12/711recommendation systemscrowdsourcing platformcognitive similaritysimilar places |
| spellingShingle | Luong Vuong Nguyen Jason J. Jung Myunggwon Hwang <i>OurPlaces</i>: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services ISPRS International Journal of Geo-Information recommendation systems crowdsourcing platform cognitive similarity similar places |
| title | <i>OurPlaces</i>: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services |
| title_full | <i>OurPlaces</i>: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services |
| title_fullStr | <i>OurPlaces</i>: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services |
| title_full_unstemmed | <i>OurPlaces</i>: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services |
| title_short | <i>OurPlaces</i>: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services |
| title_sort | i ourplaces i cross cultural crowdsourcing platform for location recommendation services |
| topic | recommendation systems crowdsourcing platform cognitive similarity similar places |
| url | https://www.mdpi.com/2220-9964/9/12/711 |
| work_keys_str_mv | AT luongvuongnguyen iourplacesicrossculturalcrowdsourcingplatformforlocationrecommendationservices AT jasonjjung iourplacesicrossculturalcrowdsourcingplatformforlocationrecommendationservices AT myunggwonhwang iourplacesicrossculturalcrowdsourcingplatformforlocationrecommendationservices |