A Sightseeing Spot Recommendation System for Urban Smart ... · recommendation system, all based on...

Journal of Civil Engineering and Architecture 13 (2019) 622-640 doi: 10.17265/1934-7359/2019.10.003

A Sightseeing Spot Recommendation System for Urban

Smart Tourism Based on Users’ Priority Conditions

Yuya Mukasa and Kayoko Yamamoto

Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo 182-8585, Japan

Abstract: The present study aimed to design, develop, operate and evaluate a sightseeing spot recommendation system as a smart tourism tool that gathers and accumulates sightseeing spot information and considers personal preferences as well as priority conditions to support tourism activities, especially in urban tourist areas. The system was developed by integrating web-geographic information systems (Web-GIS), the recommendation system and the evaluation system. Additionally, the system was operated for 4 weeks in the central part of Yokohama City in Kanagawa Prefecture, Japan, and the total number of users was 62. Based on the results of the web questionnaire survey, the system was highly useful for sightseeing activities, and further utilization of each function can be expected by continuing the operation. From the results of access analysis of users’ log data, it is evident that the system has been used by different types of information terminals just as it was designed for, and that the system has been used according to the purpose of the present study, which is to support the sightseeing activities of users. However, the number of visits to pages related to the evaluation function of sightseeing spots and submitting function of new sightseeing spot information was low. This may improve if the system operation is conducted on a long-term basis. Key words: Sightseeing spot recommendation system, Web-GIS, evaluation system, recommendation system, urban smart tourism, users’ priority conditions.

1. Introduction

In the advanced information society, a great variety

of information is transmitted via the internet and such

information can easily be transmitted, received and

shared at “anytime”, “anywhere” and with “anyone”

through various information and communication

technologies (ICT). The internet offers various

information sources including social networking

services (SNS), blogs and review pages, which are

methods used for individuals to easily transmit and

receive information. The same can be applied to

tourism, as information is often gathered using the

internet in addition to the conventional method of using

magazines and guidebooks. However, in urban tourist

areas, there is an excessive amount of information

concerning the main sightseeing spots, while it is

Corresponding author: Kayoko Yamamoto, Ph.D.,

professor, research fields: urban planning and geographic information systems (GIS).

difficult to gather information concerning other

sightseeing spots. Furthermore, among a lot of

sightseeing spots, it is difficult to appropriately select

the ones that meet personal preference. To solve such

issues, a recommendation system is needed to gather

and accumulate large quantities of information related

to sightseeing spots as well as to recommend them

according to the preferences of each individual.

On the other hand, in both developed countries and

developing countries, the construction of smart cities is

pushed forward in response to regional characteristics

in recent years. With such a worldwide tendency, the

smart tourism destinations (STD) concept emerges

from the development of smart cities [1]. In smart cities,

wisely using leading-edge technologies including ICT,

it is possible to operate, manage and renovate a variety

of infrastructures to efficiently support people’s

lifestyles. Therefore, smart tourism is an important

component of smart cities. Accordingly, especially in

recent smart cities, it is necessary that everyone should

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A Sightseeing Spot Recommendation System for Urban Smart Tourism Based on Users’ Priority Conditions

623

be able to fully enjoy sightseeing by means of the

system developed by ICT and other advanced

technologies as a smart tourism tool.

At the same time, though there are conditions to be

prioritized when deciding on which sightseeing spots

to visit, they are not always definite and may change

depending on the situation. Based on the background

mentioned above, the present study aims to develop a

sightseeing spot recommendation system that gathers

and accumulates sightseeing spot information and

considers personal preferences as well as priority

conditions to support tourism activities, especially in

urban tourist areas. More specifically, the present study

will design and develop a system that integrates

web-geographic information systems (Web-GIS), an

evaluation system and a recommendation system.

Additionally, the present study will operate and evaluate

the system and extract points to improve on.

Regarding the operation target area, Central

Yokohama City, Kanagawa Prefecture, was selected.

The first reason for this selection was because many

tourists visit Yokohama as it is a popular urban tourist

area and there is an abundance of information

submitted and released, making it difficult for tourists

to efficiently obtain the necessary information. The

second reason was that the system can be used to

recommend sightseeing spots that suit the many

preferences of each user, because of the great variety of

sightseeing spots. Lastly, as there are new sightseeing

spots discovered one after another, it can be anticipated

that the system will be used to gather and accumulate

such information and recommend sightseeing spots to

users.

2. Related Work

The present study is related to 2 study fields,

namely, (1) studies concerning tourism support

systems and methods, and (2) studies concerning

recommendation systems and methods for sightseeing

spots. In (1) studies concerning tourism support

systems and methods, Kawamura [2] proposed the use

of standard tags related to sightseeing on SNS, and set

up a website to organize tourism information of

Hokkaido, Japan on the internet. Sasaki et al. [3]

gathered information concerning local resources and

developed a system that supports the sightseeing

activities of each user. Fujitsuka et al. [4] used the

pattern mining method that lists and extracts the time

series action when touring sightseeing spots, and

developed an outing plan recommendation system.

Ueda et al. [5] generated post-activity information

from the sightseeing activities of the users, and

developed a tourism support system that shares such

information as prior information for other users.

Okuzono et al. [6] took into consideration the

preferences of several people using photos, and

proposed a system that recommends sightseeing spots.

Fujita et al. [7], Sonobe et al. [8] and Sasaki et al. [9]

proposed tourism support systems using augmented

reality (AR). Among these, Fujita et al. [7] used

Web-GIS and social media in addition to AR in order

to support sightseeing activities during normal

occasions and evacuation in case of a disaster. Sasaki

et al. [9] integrated location-based AR and

object-recognition AR and used pictograms. Aminu et

al. [10] and Tan et al. [11] developed decision support

systems for sustainable tourism planning. Aoike et al.

[12] developed a tour planning support system that

utilizes crowd information of sightseeing spots.

In (2) studies concerning recommendation systems

and methods for sightseeing spots, Kurashima et al.

[13] as well as Canneyt et al. [14] proposed a travel

route recommendation system and a sightseeing spot

recommendation system, respectively, using geotags

from picture-sharing sites. Batet et al. [15] developed

a recommendation system of sightseeing spots using

the multi-agent system. Uehara et al. [16] extracted

tourism information from the internet, calculated the

similarity between sightseeing spots from several

feature vectors, and developed a system that

recommends sightseeing spots. Shaw et al. [17] took

into consideration the location information and visit


624

history of the users, and developed a system that

presents a list of sightseeing spots near the user. Ikeda

et al. [18] integrated Web-GIS, SNS and the

recommendation system, accumulated sightseeing

spot information, and developed a social

recommendation GIS in order to recommend

sightseeing spot according to the preferences of each

user. Using this social recommendation GIS as a

foundation, Zhou et al. [19] developed a sightseeing

spot recommendation system using AR, Web-GIS and

SNS. Additionally, Mizutani et al. [20] integrated

Web-GIS, the pairing system, the evaluation system

and the recommendation system in order to develop a

sightseeing spot recommendation system that takes the

changes in the situation of users into consideration.

With the preceding studies mentioned above as

references, Yamamoto [21] and Abe et al. [22]

assumed the utilization in Japanese urban tourist areas

and developed a sightseeing spot recommendation

system using non-linguistic information. Yuan et al.

[23] proposed a travel route recommendation system

for foreign tourists who visit Japan for the first time,

considering the specific characteristics of Japanese

urban tourist areas.

Additionally, studies related to point-of-interest

(POI) recommendations among studies concerning

location-based social networks (LBSN) are also

included in the same field of the present study.

Representative examples include the POI

recommendation system proposed by Noguera et al.

[24] which is based on current location information,

and the POI recommendation system proposed by

Baltrunas et al. [25] which is based on location

information and user preference. Ye et al. [26] and

Ying et al. [27] proposed a POI recommendation

method, and Bao et al. [28] and Liu et al. [29] a

recommendation system, all based on social networks

in addition to location information and user

preferences. Yuan et al. [30] and Yin et al. [31]

proposed a POI recommendation method that takes

into consideration time and space, while Liu et al. [32]

proposed a POI recommendation method that takes

into consideration the changes in user preferences. As

the multi POIs recommendation system, Yu et al. [33]

developed a system that obtains users’ travel demands

from mobile client and generates travel packages,

while Vijayakumar et al. [34] proposed a knowledge

based recommendation system employed on the

mobile information terminal to generate personalized

travel planning. He et al. [35] proposed a framework

for the POI recommendation and the transition

interval for user’s very next move can be inferred

simultaneously by maximizing the posterior

probability of the overall transitions.

Regarding the preceding studies of (1), sightseeing

activities were not sufficiently supported as seen from

how the information provided was limited to

preparing original routes and information concerning

the surrounding area. Additionally, in the preceding

studies of (2) excluding the studies by Ikeda et al. [18]

and Zhou et al. [19], a recommendation system that

gathers and accumulates new sightseeing spot

information was not proposed and developed, as there

were no submissions of new sightseeing spot

information or sightseeing spot evaluations conducted

by users. The present study differentiates from the 2

preceding studies mentioned above, because it

provides users with the option of prioritizing

conditions when sightseeing. Additionally, the present

study demonstrates the originality by proposing a

sightseeing spot recommendation system that gathers

and accumulates sightseeing spot information and

recommends sightseeing spots with the assumption

that will be utilized in urban tourist areas. Furthermore,

using the functions of Web-GIS, the system displays

geographical conditions including the walking

distance and route to sightseeing spot from the nearest

station. Because, in Japanese urban areas, the public

transportation system is developed, main moving

means for tourists are railways and geographical

condition is an important factor to determine their

destination.

3. System

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627

arithmetic mean of evaluation values of sightseeing

spots accumulated in the database and the evaluation

values of sightseeing spots entered by users is

calculated, and a new evaluation value is produced.

Regarding the distance from the nearest station, the

evaluation value for 400 m or under is set to 5, 401-500

m to 4, 501-600 m to 3, 601-700 m to 2 and 801 m or

more to 1. This was based on the study concerning

living environment evaluation conducted by Kaido [36]

who revealed that 400-800 m is walking distance. The

distance from the nearest stations was obtained using

the route search function of Web-GIS. In this way, it is

expected that the accuracy of the recommendation

system described in the next section might increase.

3.4.3 Recommendation System

According to Jannach et al. [37] and Kamishima [38],

the recommendation methods for recommending

information that meets the user preferences from large

information groups include the collaborative

recommendation, content-based recommendation and

the knowledge-based recommendation. The

knowledge-based recommendation will be used for the

recommendation system in the present study. This is

selected to solve the cold-start problem. The cold-start

problem is where there is a lack of information that can

be offered to users, and suitable recommendations

cannot be made. In order to solve this issue, the system

creates preference information by explicitly asking for

the preferences of users in advance, and uses the

knowledge-based recommendation to make

recommendations. This recommendation method is

designed to be used in the system. When each user

registers at first, he/she is asked to rank the 8 evaluation

items (satisfaction level, distance from the nearest

station, non-crowdedness, accessibility for those with

special needs, cost effectiveness, atmosphere, amenity

and comfort/service level, recommendation degree that

were the same evaluation items of the evaluation

system mentioned in the previous section) on 5 levels,

and the preference information is created. This is set as

the user’s feature vector while the evaluation value

(evaluation information) created by the evaluation

system in the previous section is set as the feature

vector of sightseeing spots, and the degree of similarity

between these values is calculated using Eq. (1).

Afterwards, the feature vectors of the top 20

sightseeing spots in the order of the highest degree of

similarity are obtained, and from the evaluation items

used when creating the user preference information, the

top 10 sightseeing spots with the highest values

concerning priority evaluation items are recommended

to users. = ∑ ×∑ × ∑ (1)

∶ Degreeofsimilarity; ∶ UserPreference; ∶ Evaluationofsightseeingspots. 4. System Development

4.1 System Frontend

The system will implement unique functions for

users, which will be mentioned below, in response to

the purpose of the present study, as mentioned in

Section 1. In order to implement these several unique

functions, the system was developed by integrating

plural systems into a single system.

4.1.1 Viewing Function of Sightseeing Spot

Information

After logging in to the system, users can go to the

page for viewing function of sightseeing spot

information, by clicking on the “view sightseeing spot

information” in the menu bar. Fig. 3 shows the pages

for the viewing function of sightseeing spot

information. On this page, user can freely search for

sightseeing spots using either “search for sightseeing

spots on digital map” or “search for favorite category

of sightseeing spots”. In the system, in reference to

Ikeda et al. [18], Fujita et al. [6], Zhou et al. [19] and

Mizutani et al. [20], all sightseeing spots are divided

into 6 categories of “food and beverages”, “shopping”,

628

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629

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633

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634

spot information beforehand. Therefore, 180 items of

sightseeing spot information in the system developed

by Ikeda et al. [18] and Mizutani et al. [20] were

gathered and confirmed, and the information

concerning the location and operation was updated.

Next, new sightseeing spot information was gathered

referring to 4travel.jp that is a travel review site. As the

result of this process, 173 items of sightseeing spot

information were accumulated in the system

beforehand containing the ones that belong to the

category of “food and beverages” the most. Moreover,

the link to the website related to each sightseeing spot

as well as the distance from the nearest stations

calculated in Section 3.4.3 was gathered by the

administrators and entered into the system as

sightseeing spot information.

When using the recommendation function of

sightseeing spots, the evaluation values for each

sightseeing spot are required. Therefore, the website of

4travel.jp was referred in order to determine the

evaluation values. During this process, the number of

people providing reviews on the above website was

recorded for each sightseeing spot. This is used as a

parameter to recalculate new evaluation values when

users conduct evaluations for sightseeing spots.

However, if the number of people providing reviews is

extremely low, this number will be set as 1 by means of

the evaluation values obtained in the preceding studies

mentioned above.

5.2 User Assumption

Those who are planning a sightseeing trip within the

operation target area, those who have visited the

operation target area, as well as those living within the

operation target area, are assumed users of the system.

Regarding those planning a sightseeing trip within the

operation target area, it is assumed that they will gather

information concerning sightseeing spots they are

interested in by using the recommendation function of

sightseeing spots, and obtain detailed information

concerning sightseeing spot by means of the viewing

function of sightseeing spot information. Regarding

those who have visited or are living within the

operation target area, they are encouraged to evaluate

the sightseeing spots they have visited. Additionally, it

is expected that such users will also utilize the viewing

function of sightseeing spot information and

recommendation function of sightseeing spots to obtain

the information concerning new sightseeing spots they

have never visited before.

5.3 Operation

5.3.1 Operation Overview

The operation of the system was conducted over a

period of 4 weeks with those inside and outside the

operation target area. Whether inside or outside the

operation target area, the operation of the system was

advertised using the website of the authors’ lab as well

as Twitter and Facebook. Additionally, the tourism

department of Kanagawa Prefecture and Yokohama

City in addition to the Yokohama Convention and

Visitors Bureau (Yokohama City Tourism Association)

supported the present study by distributing pamphlets

and operating manuals.

Users must register their “ID” and “password” when

using the system for the first time. After the registration

is completed, users are automatically moved to the top

page where they can use various functions made

available by the system. Using “my page”, users can

change their user information and preference

information in order to receive recommendations for

sightseeing spots that suit their preferences.

5.3.2 Operation Results

Users of the system are shown in Table 1. The

system has a total of 62 users with 35 male and 27

female users. Regarding age groups, there are many

male and female users in their 20s making up 45% of

the total. Subsequently, those in their 40s and 50s were

16%, and those under 20 were 13%. There were 11 new

items of sightseeing spot information (6% of all

sightseeing spots accumulated in the system) submitted

by users. Furthermore, sightseeing spots were


635

evaluated by 23 users and the evaluation values were

updated. By operating the system on a long-term basis,

the evaluation of sightseeing spot by users can be

expected to increase.

6. Evaluation

After the start of the operation, a web questionnaire

survey and access analysis of users’ log data were

conducted in order to evaluate the system developed in

the present study.

6.1 Evaluation Based on the Questionnaire Survey

6.1.1 Overview of the Questionnaire Survey

Along with the purpose of the present study, a web

questionnaire survey was implemented in order to

conduct an (1) evaluation concerning the use of the

system and an (2) evaluation concerning the functions

of the system. The web questionnaire survey was

conducted for 1 week after the start of the operation.

Table 1 also shows an overview of the web

questionnaire survey. As shown in Table 1, 33 out of

62 users submitted their web questionnaire survey, and

the valid response rate was 53%.

6.1.2 Evaluation Concerning the Use of the System

(1) Evaluation of Suitability with the Information

Acquisition Method for Sightseeing Spots

Regarding information acquisition methods for

sightseeing spots (multiple answers allowed), 61%

were PCs, 85% were mobile information terminals, and

33% were guidebooks. Therefore, it was made evident

that many users obtain sightseeing information from

the internet using PCs and mobile information

terminals in addition to using printed mediums such as

guidebooks. This also made it clear that the system,

which obtains sightseeing spot information using PCs

and mobile information terminals, is effective.

(2) Evaluation with a Focus on the Utilization

Condition of the System

Regarding the information terminals used by users

for the system, 49% were PCs, 48% were smartphones

and 3% were tablets. Therefore, it was made evident

that the system was mainly used on PCs and

smartphones. Consequently, as shown in Section 3.2, it

was reasonable to assume that the system would be

used from PCs and mobile information terminals and

make all functions available to use regardless of the

type of information terminal.

6.1.3 Evaluation Concerning the Functions of the

System

(1) Evaluations for Each Function as well as the

Whole System

The evaluation results for each function, excluding

the recommendation function of sightseeing spots, and

the entire system are shown in Fig. 11. Regarding the

simplicity of evaluation (review) submitting, while

30% did not do it, 61% answered “I think so” or “I

somewhat think so” and 9% answered “neither”.

Regarding the suitability of evaluation items for

sightseeing spots, while 30% did not do it, 61%

answered “I think so” or “I somewhat think so” and 9%

answered “neither”. In this way, the tendency of the

answer to these two items was identical. Therefore,

though the evaluation function of sightseeing spots was

not frequently used, users were able to easily use it and

the evaluation items set beforehand were adequate.

Regarding the suitability of evaluation items for

preferences, as 85% answered “I think so” or “I

somewhat think so”, it can be said that the evaluation

items set beforehand were adequate.

Regarding the simplicity of submitting new

sightseeing spot information, while 46% did not do

it, 46% answered “I think so” or “I somewhat think so”.

Table 1 Overviews of system users and web questionnaire survey respondents.

Age groups of users 10-19 20-29 30-39 40-49 50-59 60+ Total

Number of users 8 28 5 10 10 1 62

Number of web questionnaire survey respondents 2 18 5 4 4 0 33

Valid response rate (%) 25.0 64.3 100.0 40.0 40.0 0.0 53.2

636

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637

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638

Table 2 Access methods.

Access method Number of sessions Percentage (%)

PC 65 43.1

Smartphone 83 55.0

Tablet 3 1.9

Table 3 Number of visits according to page (top 10).

Rank Page name Number of visits Percentage (%)

1 Login page 194 19.3

2 Main page 86 8.6

3 Page for the viewing function of detailed information concerning sightseeing spots 80 8.0

4 Page for user registration 76 7.6

5 Page for personal preference 71 7.1

6 Page for the viewing function of sightseeing spot information 70 7.0

7 Page for the recommendation function of sightseeing spot 49 4.9

8 Page for User guide 42 4.2

9 Page for submitting function of new sightseeing spot information 37 3.7

10 Logout page 36 3.6

6.3.2 Implementation of the Route Display Function

between Multiple Spots

It is desirable to display the route between multiple

spots selected by a user. This would be more effective

if the transportation cost and required time are also

displayed. With such additional information, it can be

expected that the system will more effectively support

the sightseeing activities of users.

7. Conclusion

In the present study, after designing and developing

the system (Sections 3 and 4), the operation (Section 5)

as well as the evaluation and extraction of

improvement measures (Section 6) were conducted.

The present study can be summarized into the

following 3 points.

(1) In order to recommend sightseeing spots based

on personal preferences and priority conditions in

urban tourist areas, as a smart tourism tool, the present

study designed and developed a system that integrated

Web-GIS, the recommendation system and the

evaluation system. The system also enabled users

efficiently gather and accumulate sightseeing spot

information. Additionally, using the functions of

Web-GIS, the system displays geographical conditions

as an important factor to determine their destination.

Yokohama City, Kanagawa Prefecture, was selected as

the operation target area, where the system was

operated and evaluation was made.

(2) The operation of the system was conducted over

a period of 4 weeks targeting those inside and outside

the operation target area, and a web questionnaire

survey was conducted towards all users. Based on the

results of the web questionnaire survey, the system was

highly useful for sightseeing activities, and further

utilization of each function can be expected by

continuing the operation. The evaluation function of

sightseeing spots and submitting function of new

sightseeing spot information are especially expected to

be utilized more by continuous operation of the system,

which may increase the usefulness of support for

sightseeing activities provided by the system.

Additionally, though the recommendation function for

sightseeing spots, which is an original function of the

system, received satisfactory reviews, the accuracy of

the recommendation system can be improved even

more if the 2 functions mentioned above are utilized

more fully.

(3) From the results of access analysis of users’ log

data, it is evident that the system was used regardless of


639

the type of information terminal, according to the

system design and in line with the purpose of the

present study that was to support the sightseeing

activities of users. However, the number of visits to

pages related to the evaluation function of sightseeing

spots and submitting function of new sightseeing spot

information was low. This may improve if the system

operation is conducted on a long-term basis.

As future study projects, the improvement of the

system based on the results in Section 6.3, as well as

the enhancement of the significance of using the

system by gaining more data from other urban tourist

areas inside and outside Japan, can be raised.

Acknowledgments

In the operation of the sightseeing spot

recommendation system and the web questionnaires of

this study, enormous cooperation was received from

those mainly in the Kanto region such as Kanagawa

Prefecture and Tokyo Metropolis. We would like to

take this opportunity to gratefully acknowledge them.

References

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[2] Kawamura, H. 2012. “Efforts to Spread Standard Tags in

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[3] Sasaki, J., Uetake, T., Horikawa, M., and Sugawara, M. 2013. “Development of Personal Sightseeing Support System during Long-Term Stay.” In Proceedings of 75th National Convention of IPSJ, 727-8.

[4] Fujitsuka, T., Harada, T., Sato, H., and Takadama, K.

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[5] Ueda, T., Ooka, R., Kumano, K., Tarumi, H., Hayashi, T.,

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