# OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services

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## Abstract

**:**

## 1. Introduction

- (a)
- We propose the OurPlaces crowdsourcing platform to collect cognitive feedback from users regarding similar tourism places.
- (b)
- We built the three-layered architecture to extract the cognitive similarities among users.
- (c)
- We deployed a recommendation system based on the cognitive similarities among users (k-nearest neighbors) extracted from the three-layered architecture.

## 2. Related Work

## 3. Crowdsourcing Platform for Measuring Cognitive Similarity

#### 3.1. Cognitive Similarity Measurements

**Definition**

**1.**

#### 3.2. Three-Layered Architecture for Cognitive Similarity

**Definition**

**2**

**Network**)

**.**A network $\langle N,{E}^{1},\dots ,{E}^{n}\rangle $ is made of a set N of nodes and n sets of objects pairs ${E}^{i}\subseteq N\times N$ the set of relations between these nodes.

**Definition**

**3**

**Distance network**)

**.**A distance network $\langle N,{E}^{1},\dots ,{E}^{n}\rangle $ is made of a set N of nodes and n sets of distance functions ${E}^{i}:N\times N\u27f6\left[01\right]$ defining the distance between nodes (so satisfying symmetry, positiveness, minimality, and triangular inequality).

**Place Layer:**In the place network $\mathbb{P}$ of place layer, the nodes and edges represent tourist places and their relations, respectively. The relations between the nodes are the similarity between tourist places. A place network $\mathbb{P}$ is a directed graph $\langle {N}_{\mathbb{P}},{E}_{\mathbb{P}}^{similarity}\rangle $, where ${N}_{\mathbb{P}}$ is the set of tourist places and ${E}_{\mathbb{P}}^{similarity}\subseteq {N}_{\mathbb{P}}\times {N}_{\mathbb{P}}$ is the set of relations between these tourist places. In this study, the relationship between tourism places was measured by using cosine similarity metric following Equation (1).**Cognition Layer:**In this layer, the cognition network $\mathbb{C}$ is determined as a network $\langle {N}_{\mathbb{C}},{E}_{\mathbb{C}}^{i}\rangle $ where ${N}_{\mathbb{C}}$ and ${E}_{\mathbb{C}}^{i}\subseteq {N}_{\mathbb{C}}\times {N}_{\mathbb{C}}$ are the set of the cognition pattern from groups of users and the relationship between these groups, respectively. These groups are determined and classified based on the cognition pattern of each user as mentioned in Section 3.1. The objective relationship between the $\mathbb{S}$ and $\mathbb{O}$ defined through the priority in selecting similar pairs of tourism places by users. These relationship are expressed by a relation: $Selections\subseteq {N}_{\mathbb{S}}\times {N}_{\mathbb{C}}$.**User Layer:**In this layer, the user network $\mathbb{U}$ consists of nodes and relations, which are users and numerous kinds of relationships, respectively. Therefore, the user network $\mathbb{U}$ is defined as a network $\langle {N}_{\mathbb{U}},{E}_{\mathbb{U}}^{i}\rangle $, in which ${N}_{\mathbb{U}}$ is a set of entity of a user and ${E}_{\mathbb{U}}^{i}\subseteq {N}_{\mathbb{U}}\times {N}_{\mathbb{U}}$ is the relationship between these entities. These relations are extracted through the objective relationship from $\mathbb{C}$ to $\mathbb{U}$ based on the extraction of user groups who have cognition pattern similarity. They can be expressed by a relation: $Extracts\subseteq {N}_{\mathbb{C}}\times {N}_{\mathbb{U}}$.

## 4. Cross-Cultural Recommendation: A Case Study

- Representing the information based on the history of user activities: The priority in selecting similar tourism places of users needs to be analyzed and modeled (user cognition pattern).
- Generating the neighbors of the active user: The cognitive similarities between users can be extracted from the three-layer architecture according to the collected datasets from users and the collaborative filtering algorithm.
- Generating tourism place recommendations: The top-N tourism places are recommended to the active user according to the activity history of the neighbors.

#### 4.1. Users Representation

#### 4.2. Generation of Neighbors

#### 4.3. Generation of Recommendations

## 5. Experiments

#### 5.1. Overview of OurPlaces Crowdsourcing Platform

- (a)
- During the first step, the OurPlaces platform displays the four types of tourism places (hotels, restaurants, shopping malls, and attractions) based on the country information, and new users then select the tourism places that they are aware of and/or have been to.
- (b)
- During the second step, the platform suggests a list of four similar tourism places (dependent on the type of place and accompanied by an $\alpha $ that is initially set to 1 for new users parameter, which is the selecting trend of the user). The user then selects a tourism place they think is similar to the one they have chosen during the first step.
- (c)
- During the final step, the platform stores cognitive feedback from the users in the database and re-calculates the $\alpha $ parameter for the suggestion process when the user has a new selection (start a new loop).

#### 5.2. Statistics of Cognitive Feedbacks

#### 5.3. Evaluation

## 6. Conclusions and Future Work

## Author Contributions

## Funding

## Conflicts of Interest

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Users | Similar Tourism Place | |||
---|---|---|---|---|

$\langle {\mathit{P}}_{1}$,${\mathit{P}}_{2}\rangle $ | $\langle {\mathit{P}}_{3}$,${\mathit{P}}_{4}\rangle $ | $\langle {\mathit{P}}_{5}$,${\mathit{P}}_{6}\rangle $ | $\langle {\mathit{P}}_{7}$,${\mathit{P}}_{8}\rangle $ | |

${U}_{1}$ | R = 0.87; K = 0.78; N = 0.67 | R = 0.59; K = 0.64; N = 0.73 | 0 | R = 0.51; K = 0.58; N = 0.43 |

${U}_{2}$ | R = 0.87; K = 0.78; N = 0.67 | R = 0.59; K = 0.64; N = 0.73 | R = 0.66; K = 0.71; N = 0.66 | 0 |

${U}_{3}$ | R = 0.87; K = 0.78; N = 0.67 | 0 | 0 | R = 0.51; K = 0.58; N = 0.43 |

${U}_{4}$ | R = 0.87; K = 0.78; N = 0.67 | R = 0.59; K = 0.64; N = 0.73 | R = 0.66; K = 0.71; N = 0.66 | 0 |

${U}_{5}$ | 0 | R = 0.59; K = 0.64; N = 0.73 | R = 0.66; K = 0.71; N = 0.66 | R = 0.51; K = 0.58; N = 0.43 |

# | Country | (#) Cities | (#) Places |
---|---|---|---|

1 | South Korea | (7) Seoul; Busan; Daegu; Deajeon; Geoje; Incheon; Ulsan | 1306 |

2 | Vietnam | (3) Hanoi; Ho Chi Minh; Danang | 670 |

3 | Singapore | (1) Singapore | 240 |

4 | Thailand | (4) Bangkok; Phuket; Chiang Mai; Pattaya | 750 |

5 | India | (2) New Delhi; Mumbai | 334 |

6 | Japan | (4) Tokyo; Osaka; Kyoto; Fukuoka | 889 |

7 | China | (4) Beijing; Shanghai; Hong Kong; Macau | 1012 |

8 | England | (5) London; Manchester; Cambridge; Liverpool; Birmingham | 1211 |

9 | Spain | (5) Barcelona; Madrid; Seville; Malaga; Granada | 1128 |

10 | Germany | (3) Berlin; Munich; Hamburg | 875 |

11 | France | (3) Paris; Nice; Lyon | 768 |

12 | Greece | (2) Santorini; Athens | 305 |

13 | Austria | (2) Vienna; Langenfeld | 264 |

14 | Italy | (4) Rome; Milan; Venice; Turin | 657 |

15 | United States | (6) New York; Los Angeles; Miami; Chicago; Washington; San Francisco | 2083 |

16 | Brazil | (2) Rio de Janeiro; Sao Paulo | 330 |

17 | Argentina | (3) San Carlos de Bariloche; Pinamar; Buenos Aries | 453 |

18 | Colombia | (3) Bogota; Pereira; Salento | 450 |

19 | Uruguay | (2) Montevideo; La Paloma | 321 |

20 | Chile | (2) Santiago; Punta Arenas | 304 |

21 | Mexico | (2) Mexico City; Oaxaca | 332 |

22 | Canada | (4) Vancouver; Toronto; Montreal; Quebec City | 750 |

23 | Australia | (5) Melbourne; Sydney; Brisbane; Newcastle; Adelaide | 1200 |

24 | New Zealand | (6) Auckland; Queenstown; Wellington; Paihia; Wanaka; Hamilton | 1654 |

Total | 84 cities | 18.286 |

Number of Neighbors | MAE | RMSE | ||
---|---|---|---|---|

UBPS | Proposed Method | UBPS | Proposed Method | |

5 | 0.809 | 0.784 (+2.5%) | 1.203 | 1.162 (+4.1%) |

10 | 0.782 | 0.761 (+2.1%) | 1.145 | 1.108 (+3.7%) |

20 | 0.774 | 0.755 (+1.9%) | 0.997 | 0.964 (+3.3%) |

30 | 0.771 | 0.757 (+1.4%) | 0.854 | 0.823 (+3.1%) |

50 | 0.757 | 0.746 (+1.1%) | 0.791 | 0.769 (+2.2%) |

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## Share and Cite

**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 Vuong, 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