CT-Planner4: Toward a More User-Friendly
Interactive Day-Tour Planner
Yohei Kurataa, andTatsunori Haraba

Department of Tourism Science, Graduate School of Urban Environmental Sciences
Tokyo Metropolitan University, Japanykurata@tmu.ac.jpbResearch into Artifacts, Center for Engineering (RACE)The University of Tokyo, Japan
hara_tatsu@race.u-tokyo.ac.jp

Abstract
It is often difficult for novice people to make a tour plan for a tight schedule. Hence, we havedeveloped a computer-aided tour planner, which enables the user to design a tour plan with thesystem in a collaborative manner. This paper introduces its latest version, CT-Planner4, whichbecomes accessible via Internet and is improved to achieve more user-friendliness. CTPlanner4 mainly targets foreigners and is expected to stimulate their hidden/unattended needsof plan consultation. Our two user tests with international students and foreigners living abroad,and another interviews with tourist advisors substantiates the practicality of CT-Planner4.
Finally, we propose the application of its user log to marketing analysis.
Keywords: computer-aided tour planning; personalization; cold start problem; user evaluation

1 Introduction
Thanks to the widespread use of ICT in tourism, people can travel the world much
more casually. On the other hand, it is still difficult for novice people to make a tour
plan for a tight schedule, especially when they are visiting an unfamiliar area withvarious attractions. Of course, many people still enjoy tour planning, but at the sametime some people feel anxious about it, wondering whether their plan is reasonable,
without wasting or lacking for time. In addition, when in a foreign country, it is notalways easy to ask for somebody’s advice.
In order to relieve people from such difficulty in tour planning, researchers haveproposed several systems that can make personalized tour plans, (e.g., Ardissono etal., 2003; Goy & Magro, 2004; Maruyama et al., 2004; Kramer et al., 2006; Lee, etal., 2007; Castillo et al., 2008). However, many systems aim primarily at the
automation of tour planning and, as a result, tended to exclude the user’s participationin the process of planning (Seifert 2008). To overcome this problem, some systems
introduced a customization phase, in which people can modify recommended plans(i.e., insert/remove/replace/reorder the POIs in the plans) (Garcia et al., 2010;
Schaller, 2011). Moreover, Kurata (2010) developed CT-Planner, which enables theuser to design a tour plan with the system in a collaborative manner. CT-Plannerstands for Collaborative Tour Planner, and also City Tour Planner as it mainly targets
city-scale day trips. Its interaction model is similar to that of critiquing-based
recommender systems (Linden et al., 1997; Chen and Pu, 2009) in the sense that theusers are required to gives certain feedbacks about the system’s recommendations, butthe users’ feedback in CT-Planner is not evaluations of recommended plans, but
additional requests which come up to the users’ mind, inspired by those plans.
In this paper we introduce the latest version of CT-Planner, namely CT-Planner4. CTPlanner4 is a web-based application written in JavaScript and accessible from various
devices (PCs, tablets, etc.) via Internet (http://ctplanner.jp), although special support
for smartphones’ tiny screen is not yet implemented. CT-Planner4 succeeds several
essential features from its previous version: i) collaborative planning ii) web-based
accessibility, and iii) a genetic algorithm which allows high-speed plan calculation. In
addition, to realize more user-friendliness, it newly achieves: i) introduction of a hotstart mechanism, ii) improvement of user profile specification, and iii) support ofopen/close hours and walking preference. Through an overview of CT-Planner4 andits evaluation result, this paper demonstrates the practicality of computer-aided
collaborative tour planning. Note that in this paper, tour plans refer to the plans that
visit some of many POIs distributed in a city-scale area in a few hours or a day. Thismeans that transportation to/from the destination and lodging in it are out of our scope.
The remainder of this paper is structured as follows: Section 2 reviews key ideas ofcomputer-aided tour planning. Section 3 describes the user interface of CT-Planner4,
while Section 4 explains the underlying mechanism. Sections 5-6 report the result of
our user tests and that of our interview with experts, respectively. Finally, Section 7
concludes with a discussion of future work.

2 Issues of Computer-Aided Tour Planning
There are at least three major issues discussed in the previous studies of computeraided tour planning: user profiling, POI evaluation, and user participation. User
profiling is the process to obtain the user’s data necessary for estimating thepersonalized value of POIs/plans. The obtained data is, for instance, his/her favourite
POI categories (e.g., Kramer et al., 2006), tour purposes (e.g., Kurata, 2010), andeven demographic properties (e.g., Lee et al., 2007). Early recommender systems
tended to force the user to input his/her profile manually at the beginning of use.
Kurata (2000) criticized the difficulty of such manual input and proposed the use of
AHP to deduce the user’s preference from the answer to the questions comparing tour
motivations. Similarly, Kurata (2010) used AHP to deduce the user’s preference,asking him/her the preference over pairs of actual plans with different features. In
Kurata (2011)’s user test, however, most users preferred manual input of preference
when both options were available. This implies that the essential problem of user
profiling is not manual input, but rather so-called cold start—the user is forced tospend long time to report his/her profile before getting recommendation. To prevent
this problem, CT-Planner4 asks only two questions at the beginning and from theanswer it roughly determines the initial value of user profile (Section 3).
POI evaluation is the process to estimate the personalized value of each POI. Previous
systems adopted several techniques for this, such as matching of feature vectors (e.g.,
Lee et al. 2007; Kurata, 2011), content-based matching using an ontology (e.g.,
Kramer et al., 2006), collaborative filtering (e.g., Castillo et al., 2008), and direct
input by the user (Maruyama et al., 2004). In the process of POI evaluation, we
should be careful about the context-dependency of POIs’ value (Baltrunas et al. 2010;

Kurata, 2011)—the same tourist may give different evaluations to the same POI
depending on his companion, weather, and season. Thus, it is desirable that thesystem allows the user to customize the POI evaluations in a certain way (e.g.,
accepting the request of must-see POIs).
User participation is a critical issue that many computer-aided tour planning systems
have left behind (Seifert 2008). Lee et al. (2007) insists that the user-systeminteraction should be minimized in tour planning systems, but this claim isquestionable. Kramer et al. (2006) insists that most people are not always aware of
their preference and thus need to get reminded of it. We believe that many people
cannot tell their preference/interest before seeing sample choices, especially when thetarget is not what they often buy, such as tourism products. CT-Planner (Kurata,
2010), therefore, adopted a cyclic approach where the system proposes a sample plan,
the user examines it and gives feedback, and then the system revises the plan
accordingly. The merit of this approach is that the user is not forced to specify theirrequest/interests all at once in the early stage of use. This approach is welcomed by its
test users (Kurata, 2011) and thus succeeded to CT-Planner4.

3 User Interface of CT-Planner4
CT-Planner4 starts from the screen in Fig. 1. Here you are asked only two questions:
your destination and favourite travel style. Currently we are listing six destinations
in/near Tokyo and five travel styles: Enjoy Various Attractions, City Walking, Stroll
in Relaxing Places, Learn Localities, and Walk with Children. These travel styles arederived from the result of a GPS-based activity survey on foreign tourists visiting
Tokyo (Aratani et al., 2012).

Fig. 1. Initial screen of CT-Planner4
If you select “Yokohama” and “Walk with Children”, for example, you will see themain screen in Fig. 2. It shows the route of a sample tour plan on the central map,which strolls across the central Yokohama in three hours. Its agenda is also shown on
the right end of the screen. The map is illustrated with the aid of Google Maps API
and accordingly, you can zoom/scroll the map and even see the corresponding
satellite image to check the detail of the tour route.
The left end of the screen shows three items from the top: tour condition, user profile,and command bottoms. The tour condition consists of five items: duration, start time,
day of the week, walking speed, and reluctance to walk. If you modify the tour
condition, your plan is revised promptly. For instance, if you set the start time to
5:00pm, your plan will skip most museums because they are already closed. Similarly,
if you set the reluctance to walk to yes, the walking distance of your plan will become
shorter. The user profile consists of focus and taste parts, each represented by a fiveaxial radar chart and a set of four sliders. The values of your profile is pre-determinedbased on your initial selection of favourite travel styles, but they are adjustable if you
want. For instance, if you put more weight on culture by clicking the right-upper partof the radar chart, your plan will visit more museums. If you slide the top slider to theright end (i.e., to Well-known side), your plan will visit famous POIs more likely.

Fig. 2. Main screen of CT-Planner4
If you click a POI’s name on the map or the agenda, a small info-window appears at
its location (Fig. 3). This window shows the POI’s basic information (name, estimatedvalue represented by 1- to 5-stars, description, estimated staying time, and hyperlinks
to the related websites). The window also shows several buttons. Once you click Visit
button, the system generates the plans which visit this POI as long as possible.
Conversely, once you click Avoid button, the system no longer shows the plans that
visit this POI. Start/Goal button allows you to set this POI as the start/goal location ofyour tour. Finally, +10/-10 button allows you to adjust the staying time. Note that you
can request POIs to visit/avoid, but the system does not force you to do so. In otherwords, you can let the system select other unrequested POIs to visit if you have time.

CT-Planner4 expects the following interaction cycle: (i) you look at a sample plan and
POIs shown on the map, (ii) you provide a certain response (specify a POI tovisit/avoid, changes your tour condition, or modify your profile) and (iii) the systemrevises the displayed plan accordingly and displays it. This cycle is repeated until you
get satisfied with the plan. You can also use undo/redo commands. After finishing,
you can print out your plan and bring it to your destination.

Fig. 3. An example of a POI’s info-window

4 Underlying Mechanisms
4.1 Deriving the Best Plan
The problem to find out the best tour plan under given constraints can be formalized
as follows (Kurata, 2010):
Given a complete graph V , E  , the expected utility of each node ui , the visitation
time t visit i , the travel time t travel ij , origin node vori V , goal node v goal V , and time

constraint T , find a series of nodes to be visited va ,, va va V that maximizes
1kikthe sum of utilities  ua under the following three constraints:
i 1

 t
k

i 1

visit

i

 i  0 t travel ai a j  T , va0  vori , vak 1  vdes
k 1

ai

This problem is essentially Selective Traveling Salesman Problem (STSP), which isproved to be NP-hard (Laporte & Martello, 1990). Thus, to compute semi-optimal
solutions of this problem, CT-Planner 4 uses the following genetic algorithm:
Step 1. Initialization: We generate n initial plans, which starts at the given startlocation, visits an appropriate number of randomly-selected POIs, and ends at thegiven goal location.
Step 2. Evolution: The following process is repeated N times:
Crossover: We select two plans PA and PB, randomly from the existent plans, andcreate their hybrid plan (i.e., the plan that combines the random length of PA’s fastpart and that of PB’s last part and then removes redundancy of POIs). This is repeatedtimes ( : crossover rate). As a result, we have 1plans.
existent plans and replace a POI
Mutation: We select a plan PC from the 1
times
visited in PC by another randomly-selected POI. This is repeated 1( : mutation rate).

Survival Competition: We select two plans randomly from the 1plans and remove the one with a lower score. This is repeated times.
Step 3. Selection of the best plan: From thethe highest utility score.

existent

existent plans, we select the one withThis algorithm has four parameters: number of generations , number of survivingplans in each generation , crossover rate , and mutation rate . As andincrease, the solution becomes better, but the computation time increases. We tuned
up the parameters such that the computation usually finishes within a second. For
powerless devices, CT-Planner4 has another option with which the above
computation is conducted on our server.
The above algorithm allows us to handle POIs’ closing days/hours, simply by
reducing the score of violating plans (i.e., the plans that require to visit a POI during
its closing hours/days) to zero. In a similar way, the above algorithm will allow us tohandle various extensions, such as POIs’ time-dependent values, budget limitation,and reciprocal effects of POIs (Section 4.2), although they are not implemented yet.
4.2 Evaluating Plans
In the above algorithm, we have to score the plans for comparison. Basically, a plan’sscore is calculated as the total of the expected utility of POIs to be visited in this plan.
Then, certain penalty is imposed on the score when the plan requires long-distancewalk, depending on the user’s setting on reluctance to walk (Fig. 2).
This scoring method may have a room for improvement, because in reality the plan’svalue may be affected by the order of POIs. For instance, people may prefer the planthat visits the highlight POI at the end rather than at the beginning. In addition, we
should also consider reciprocal effects— if people visit similar POIs repeatedly, they
may get bored, or conversely, they may get more understanding and satisfaction.
Ideally, tour plans should be designed carefully, like fabricating a story, imagining the
temporal transitions of tourists’ emotions. How to realize such tour design incomputers is left as a future question.
4.3 Evaluating POIs
The expected utility of a POI is estimated based on the vector matching of the POI’s
characteristics and the user’s profile (Kurata, 2010). The POI’s characteristics areevaluated in advance by the data author (Section 4.4), while the user’s profile isdetermined at the time of use (Section 3).
CT-Planner4 supports two types of user profile: focus and taste. Focus refers to thetour’s functional feature demanded by the user. It is represented by the assignment ofweight to the five categories: culture, entertainment, nature, art, and shopping. Taste
refers to the tour’s emotional characteristics demanded by the user. We considered
four types of taste, each represented by its two poles; less-known or well-known, calm
or energetic, local or universal, for adult or with kids. Taste is newly added in thisversion to enrich the representation of user preference. We expect that the taste isrelatively stable and consistent, while the focus often changes depending on thedestination and context.

Each POI is evaluated from the nine criteria corresponding to the parameters of focus
and taste. For instance, a typical zoological park has high scores for entertainment andnature, and it is positioned at energetic, universal, and with kids sides, respectively. In
order to assure the reliability of such evaluation, a scoring manual was prepared.
Note that the estimated utility of some POIs may be replaced internally for thecomputation of tour plans. For instance, when you request to visit a POI px, very highutility is assigned to px, such that CT-Planner4 generates the plans that visit px as longas the time constraint allows. Conversely, when you request to avoid a POI py, its
utility is set zero, such that the generated plans do not visit py.
4.4 Authoring Destination Data
We made an Excel-based data editor, with which people can easily make a destination
data for CT-Planner4. In future, we will ask volunteers to author destination data, inorder to expand the destinations that CT-Planner supports. In our editor, the user is
asked to input in a table the basic data of each POI, including its evaluation and geocoordinates. Then, in order to save the user’s task, the editor automatically derives all
inter-POI routes by walk with the aid of Google Directions API and records them injson format. Pre-computation of inter-POI routes is highly effective for the reductionof computation time in use (Joest and Stille, 2002), and also necessary to overcome
the API’s access limit per day.

5 Evaluation by Test Users
In order to evaluate CT-Planner4’s practicality, we conducted two user test, one withinternational students and another with foreigners living abroad, because we wanted
to examine whether our tool was useful for both people who were already in/near thedestination (on-destination users) and those who were at home (off-destination users).
The reason for targeting foreigners is that they tend to feel difficulty in tour planning
due to language barrier and lack of geographical knowledge. As the first step of our
test we employed international students instead of actual travellers because we could
easily control their travel schedule.
The test with on-destination users was conducted on March, 2013. We advertised on a
student mail magazine and employed 16 international students from 8 countries. We
assembled them to Yokohama, a popular destination near Tokyo. At a meeting room,
they were asked to make their tour plan with CT-Planner4 and then to answer aquestionnaire. After that, they went sightseeing for six hours. After returning, we
asked them to answer another questionnaire and conducted a short interview.
The test with off-destination users was conducted on August, 2013. We employed 56
English-speaking test users from 28 countries, introduced by an investigation firm.
We asked them to use CT-Planner4 on their PCs for at least five minutes and then to
answer an online questionnaire. They were not given any instruction about how to use
CT-Planner4, so that we can examine the intuitiveness of the system.
5.1 Satisfaction and Impression
The participants of both tests were asked their satisfaction with and impression of CTPlanner4 (Table 1). Most users evaluated CT-Planner4 positively. Its easiness wasevaluated even by off-destination users who have no instructions (Q2). The use of
CT-Planner4 gives a positive effect, although not dramatic, on their expectation (Q4).
In addition, most users agreed the system’s positive effect on learning destinations(Q5-6). Unfortunately, on-destination users show a bit lower satisfaction (Q1). This isprobably because at the time of their test, undo and reverse functions was not
implemented yet and some users strongly requested them.
We investigated the relations between overall satisfaction (Q1) and such personalfeatures as travelling days per year, experience to visit Japan, interest in Japan, andself-evaluation of map reading skill, but no clear relation was found. In addition, theunsatisfied users did not have any remarkable features in their profile.
On-destination users were also asked to evaluate CT-Planner4 after their six-hour tour
(Q7-8). Q7 (usefulness of tour plans) is agreed by most users, but interestingly, Q8(usefulness of tour planning experience) gets more agreement. Indeed, some users
reported in the interview that they could flexibly reschedule their plan during their
tour thanks to their planning experience. This indicates a unique aspect of CTPlanner4 as an educational tool for tourists.
Table 1. Satisfaction and impression by users (average scores in five-grade scale).
QuestionOn-destinationusers

Off-destination
users

Q1

How satisfied are you with CT-Planner4 overall?
(5: very satisfied – 1: very dissatisfied)

3.81

3.96

Q2

Was it easy to use? (5: very easy – 1: very hard)

4.13

3.96

Q3

Were you able to draw up a travel plan to your taste?
(5: strongly agree – 1: strongly disagree)

4.00

3.93

Q4

Did use of this tool heighten your expectations of sightseeing in
Yokohama / Japan? (5: very much – 1: lowered very much)

3.73

3.88

Q5

Do you feel clearer than before what you’d like to do at thedestination? (5: strongly agree – 1: strongly disagree)

3.94

3.89

Q6

Did use of this tool hep you notice tourist spots which you would
not consider in your usual planning? (5: very much – 1: not at all)

4.19

4.13

Q7

Was the tour plan you made with CT-Planner4 useful for yourtrip? (5: strongly agree – 1: strongly disagree)

4.19

–

Q8

Was your experience of computer-aided tour planning useful foryour trip? (5: strongly agree – 1: strongly disagree)

4.38

–

5.2 Demand by Situations
We proposed six situations where CT-Planner4 can be used, and asked the test users
the level of their demand on CT-Planner4 in each situation. We found that CTPlanner4 is welcomed in most situations except the earliest phase (S1). This meansthat people start to want CT-Planner4 after deciding their destination. Thus, theavailability of CT-Planner4’s service may not give high impact on the selection ofdestinations. Interestingly, on-destination users had lower demand for on-site mobileuse (S6). Some users said that they had no time to consult on their trip plan once theystarted travelling. Another finding is that in most situations (S1-5) on-destination
users had higher demand than off-destination users. This may indicate that the actual
practice of tour plans made with CT-Planner4 increases their evaluation of it.
Table 2. How much people want to use CT-Planner4 in each situation(average scores in five-grad scale, where 5: very much – 1: not at all)On-destinationusers

Off-destinationusers

S1 At home, when selecting a destination from several candidates

3.81

3.61

S2 At home, when planning activities at the selected destination

4.50

3.80

S3 In the hotel at the destination, on the previous night of strolling

4.50

3.80

S4 On board a transportation medium headed for the destination

4.06

3.96

S5 From a device at a local tourist-information agency

4.13

3.75

S6 While strolling around the city (using a mobile device)

3.88

4.07

Situation

5.3 Comparison with Usual Planning
We asked the off-destination users to compare tour planning with CT-Planner4 andtheir usual planning (i.e., the planning style they normally use, maybe manual orcomputer-aided). The result shows that the highest strength of CT-Planner4 is its
time-saving capability, followed by plan’s novelty and reliability (Table 3). On theother hand, the plan’s quality is not highly evaluated. Actually, 30% of the test users
answered that their usual planning is superior to CT-Planner4 with regard to plan’squality. This implies that there is still room for improvement in our current plan
generation method.
Table 3. Evaluation of CT-Planner4 in comparison with usual tour planning (+2:Planning with CT-Planner is much better – -2: Usual planning is much better)
Criteria

Off-destination users

Plan’s quality+0.14

Plan’s reliability+0.65

Plan’s novelty+0.79Time spent to make plans+1.09

5.4 User Requests
We asked the off-destination users to write any request to CT-Planner4. The popular
requests were (i) to display more information and/or pictures about POIs, (ii) toprovide a user instruction, and (iii) to add restaurant/café/food information. In
addition, we got several useful requests, such as (i) to simplify the map with icons
with automatic pop-ups, (ii) to show other user’s comments on POIs (possibly using
an outer service like Trip Advisor), and (iii) to consider weather forecast for planning.

The request for restaurant/café/food information is also often heard in the interviews
with the on-destination users. CT-Planner4 did not support restaurants/cafés yet,because the current method required us to pre-compute the routes between all pairs of
POIs (Section 4.4) and thus it is difficult to add a number of restaurants/cafés to the
POIs. However, as food is an important content of tourism, we are seeking analternative technique which can treat restaurants/cafés differently from other POIs.
Some users proposed the use of public transportation during tours. Actually, we canderive the routes using public transportation making use of Google Maps API.
However, it often shows the route that uses local low-frequent buses, which isprobably difficult for novice tourists and thus, the current version did not consider theuse of public transportation. In future, we are going to calculate inter-POI routes bydifferent transportation modes, and we may adopt the one by public transportation if it
is found much time-saving, high-frequent, and easy for novice tourists.
5.5 Analysis of User Log
CT-Planner4 records the user’s commands and the resulting plans, and uploads themto our server if possible. Through the above two tests, we obtained eight complete
logs by on-destination users and thirty-five by off-destination users. These logs showthat on average the on- and off-destination users have spent 20 min. 50 sec. and 6 min.
42 sec. on CT-Planner4, respectively. The on-destination users spent much longer
time because they carefully designed their plan for their immediate use, while the offdestination users simply tried this tool to see what they can do with it. Fig. 4 shows
the detail of the users’ commands. On average the on- and off-destination users gave
70.1 and 31.6 commands, respectively. The difference arises mainly from thefrequency of opening POI info-windows and the following visit/avoid request. This is,again, because the on-destination users carefully designed their plan for that day.
Interestingly, the numbers of commands gave by both user groups for the setting of
tour condition setting and for that of user profile are almost the same.
Our more detailed analysis revealed the following typical pattern of the use of CTPlanner4: (i) at the beginning phase the user often use the commands for setting tour
conditions and user profile, (ii) in the middle phase the user often open POIs’ infowindows and give visit/avoid requests and (iii) at the last phase the commands areused less frequently, but some users experimentally re-adjust their profile to see what
happens then.
1.0
On‐Destination Users

7.3

Off‐Destination Users

6.9
0

14.0

11.3
10

Tour Condition Setting

34.8

8.5
20

User Profile Setting

2.6

13.1

2.3

30

40

Opening a POI's Inforwindow

50

60

Visit/Avoid RequestingFig 4. Frequency of commands gave by test users

70
Other

6 Evaluation by Experts
In addition to the two user tests in Section 5, we had an interview with four staffs at
Yokohama’s tourist information office on July, 2013. All of them welcomed CTPlanner4, saying that it would support unattended or hidden consultation needs. They
regretted that many tourists stayed their office only for a short time, often without
asking any question, especially when the staffs looked busy. In addition, they said thatthe staffs could attend only visitors who came to the office on their office hours, while
tourists often had consultation needs in the previous night of sightseeing. As for theplan’s quality, they said that CT-Planner4 apparently generated reasonable plans,
although it would be much better if it supports the use of subways and Akai Kutsu
Buses (bus service specialized for tourists).
One of the staffs pointed out that CT-Planner4 would be very useful when they wereasked about other cities. They have abundant and up-to-date knowledge about
Yokohama, but not so much about other cities, such as Tokyo and Kyoto, whereas
tourists’ questions often go beyond their speciality. Another staff suggested to provide
CT-Planner4 to taxi drivers and hotel staffs, who often got questions from foreign
tourists but had not received any special training for advising them. Those ideas areworth considering when promoting CT-Planner4 to the business world.

7 Conclusions and Future Work
This paper reported the latest version of our computer-aided tour planner, CTPlanner4. Our goal is to provide tour planning aids to novice tourists via the Web.
With CT-Planner, people can consult on their tour plan from anywhere at any time, asmuch as they want, without worrying about asking people in foreign languages. Its
practicality is substantiated by the result of our user tests and expert interview. In
addition, we observed that CT-Planner4 stimulated its users’ expectation to thedestination. We hope that in future CT-Planner4 will contribute to its destinations bybringing more tourists to there.
Although we have not explicitly claimed, we have another future goal—to collect alarge volume of user log data and make use of it for marketing analysis. The user log
of tour planning service is an invaluable source to know users’ travel needs andpreferences (Not & Venturini, 2011). For instance, our user log shows that Yamashita
Park and Red Brick Warehouse are popular for both on- and off-destination users (i.e.,most of their plans visit these two POIs), but Chinatown is popular only for ondestination users. As the number of users increase, we can derive more reliableinsights, as well as we can analyse the difference of tourist groups (e.g., Asians andEuropeans), without paying a survey cost. We expect that such findings will help
destination management offices to consider their promotion strategies, as well as
travel agencies to improve their package tours (Hara, et al., 2012).

References
Ardissono, L., Goy, A., Petrone, G., Segnan, M., & Torasso, P. (2003). Intrigue: Personalized
Recommendation of Tourist Attractions for Desktop and Hand Held Devices. Applied
Artificial Intelligence, 17(8-9), 687-714.

Aratani, K., Shimada, S., Ota, J., & Hara, T. (2012). Classification of Inbound Tourist
Activities Using GPS Log Data Toward Service Innovation. In 12th Japan-Korea
Design Engineering Workshop (pp. 28-31).
Baltrunas, L., Ludwig, B., Peer, S., & Ricci, F. (2012). Context Relevance Assessment andExploitation in Mobile Recommender Systems. Personal and Ubiquitous Computing,16(5), 507-526.
Castillo, L., Armengol, E., Onaindía, E., Sebastiá, L., González-Boticario, J., Rodríguez, A., &Borrajo, D. (2008). SAMAP: An User-Oriented Adaptive System for Planning Tourist
Visits. Expert Systems with Applications, 34(2), 1318-1332.
Chen, L., & Pu, P. (2009). Interaction Design Guidelines on Critiquing-Based Recommender
Systems. User Modeling and User-Adapted Interaction, 19(3), 167-206.
Garcia, A., Arbelaitz, O., Linaza, M. T., Vansteenwegen, P., & Souffriau, W. (2010).
Personalized Tourist Route Generation. In Current Trends in Web Engineering (pp.
486-497). Springer Berlin Heidelberg.
Goy, A., & Magro, D. (2004). STAR: A Smart Tourist Agenda Recommender. In
Configuration Workshop at ECAI (pp. 189-208).
Hara, T., Shimada, S., Yabe, N., Kurata, Y., Aoyama, K. and Hompo, Y. (2012). Value CoCreation in Tourism: Incorporating Non-Expert's Design into Expert’s Design Activities.
In 1st International Conference on Human Side of Service Engineering.
Joest, M., & Stille, W. (2002). A User-Aware Tour Proposal Framework Using a Hybrid
Optimization Approach. In 10th ACM International Symposium on Advances inGeographic Information Systems (pp. 81-87). ACM.
Kramer, R., Modsching, M., & ten Hagen, K. (2006). A City Guide Agent Creating and
Adapting Individual Sightseeing Tours Based on Field Trial Results. International
Journal of Computational Intelligence Research, 2(2), 191-206.
Kurata, Y., Sadahiro, Y., & Okunuki, K. (2000) Development of a Preference-Based Tour
Planning System. In: 9th Conf. of GISA Japan (pp. 199-202). in Japanese.
Kurata, Y. (2010). Interactive assistance for tour planning. In Spatial Cognition VII (pp. 289302). Springer Berlin Heidelberg.
Kurata, Y. (2011). CT-Planner2: More Flexible and Interactive Assistance for Day Tour
Planning. In ENTER 2011 (pp. 25-37). Springer Berlin New York.
Laporte, G., & Martello, S. (1990). The Selective Travelling Salesman Problem. Discrete
Applied Mathematics, 26(2), 193-207.
Lee, J., Kang, E., & Park, G. (2007). Design and Implementation of a Tour Planning System for
Telematics Users. In ICCSA 2007 (pp. 179-189). Springer Berlin Heidelberg.
Linden, G., Hanks, S., & Lesh, N. (1997). Interactive Assessment of User Preference Models:
The Automated Travel Assistant. In 6th Intl. Conf. on User Modelling (pp.67-78)
Maruyama, A., Shibata, N., Murata, Y., Yasumoto, K., & Ito, M. (2004). A Personal Tourism
Navigation System to Support Traveling Multiple Destinations with Time Restrictions.
In Advanced Information Networking and Applications 2004 (Vol. 2, pp. 18-21). IEEE.
Not, E. & Venturini, A. (2011). The Unexploited Benefits of Travel Planning Functionalities: a
Case Study of Automatic Qualitative Market Analysis, In eRTR, ENTER 2011.
Schaller, R. (2011). Planning and Navigational Assistance for Distributed Events. In 2nd
Workshop on Context Aware Intelligent Assistance, Berlin, Germany.
Seifert, I. (2008). Collaborative Assistance with Spatio-temporal Planning Problems. In Spatial
Cognition V (pp. 90-106). Springer Berlin Heidelberg.

Acknowledgements
This work is supported by JST RISTEX’s research program on Needs-oriented
Service Science, Management and Engineering (project title: “Architecting service
with customer participation based on the analysis of customer experience and design
processes: sophisticating tour design processes as a case study”).