=Paper=
{{Paper
|id=Vol-1784/w6
|storemode=property
|title=Proposal of a Recommendation System Tourism in Ciudad Juarez
|pdfUrl=https://ceur-ws.org/Vol-1784/w6.pdf
|volume=Vol-1784
|authors=Karina Hernandez-Casimiro,Alberto Ochoa,Rosa Suarez,Raymundo Camarena
}}
==Proposal of a Recommendation System Tourism in Ciudad Juarez==
https://ceur-ws.org/Vol-1784/w6.pdf
Proposal of a Recommendation System Tourism in Ciudad
Juarez
Karina Hernandez- Alberto Ochoa Rosa Suárez Raymundo Camarena
Casimiro Universidad Autónoma de Universidad Autónoma de Maestría en Cómputo
Universidad Autónoma de Ciudad Juárez Ciudad Juárez Aplicado.DEyC, IIT.UACJ
Ciudad Juárez alberto.ochoa@uacj. al147855@alumnos.u ray.camarena@gmail.
al159045@alumnos.u mx acj.mx com
acj.mx
Sarahí Peralta
Universidad Autónoma de
Ciudad Juárez
al150298@alumnos.u
acj.mx
ABSTRACT yourself. When time is limited a good guide is essential that advice
There is a large class of Web applications associated with trip and allow them to make a selection (Plaza, 1997). This is where the
planning involving prediction according to user responses and Internet use as a means to get tourist information about the place
recommendations made in previous trips and associated with travel you visit [7].
options required by the tourist (user). This type of innovative Recommendations systems are tools that generate
applications is called: an intelligent recommendation. In this recommendations on a particular object of study, from the
research, we analyzed a survey instrument presented as the most preferences and opinions given by users. The use of these systems
important examples of trips made in a frontier society. However, is becoming increasingly popular in Internet because they are very
for the problem is properly focused, two good examples of a useful to evaluate and filter the vast amount of information
recommendation system can be presented as: available on the Web in order to assist users in their processes
1. Offer travel relators potential tourists and submit it online, based information retrieval. In this research we will have a review of the
on a prediction of previous recommendations of users with similar fundamental characteristics and aspects related to the design,
profiles associated with the user's interests. implementation and structure of recommendation systems
analyzing various proposals that have appeared in the literature.
2. Offer tourists associated with different services and products
associated with the trip to perform and suggestions online about The internet provides a lot of search engines that provides tourist
what they want to buy, based on your purchase history and / or information, such as; Augmented reality, reservation systems, e-
products in searches performed by them in other previous trips. commerce, web 2.0, big data, among others [16]. However, the
results presented are regularly inadequate because they do not
Recommender systems use a number of different technologies. We consider specific information for each individual, for this reason
can classify these systems into two groups (Systems flirtation emerge recommender systems, which considering specific
content and based on the context of the required information information for each individual build a user profile to provide the
systems). This proposed system uses contextual travel information. resource that is supposed Fittest to your need and preference [9].
Keywords Considering the above, in this research, the methodology for the
Recommender systems; user; planning tourist Ciudad Juarez. design of a recommendation system that allows providing
information tourism resources as a tool for visitors, which
1. INTRODUCTION recommended attractions you can visit, suggest itinerary of
Today tourism is developed in a dynamic and changing context, activities described, accommodation, buying souvenirs, among
which is important to adapt to technological advances, as more and other services and products that can be enjoyed, all according to the
more tourists frequently rely on them when making a trip [13]. This different profiles that may have tourists and so this enjoy your day
is mainly due to the daily use made today of technology such as the stay and also recommend to their known experience in place
internet. visited, promoting the tourism boom in Ciudad Juarez.
When tourists travel to any country, region or city for sightseeing, 2. CURRENT PANORAMA TOURISM
you want to make the most of your visit and see the greatest Tourist activity represents 9% of world GDP, Mexico ranks as one
possible number of things and interesting places. If you have of the ten countries that receives more tourists globally (SECTUR,
enough time, it is exciting to go slowly and discovering them for
2014). Mexico considers the national tourism as one of the four 3. Collaborative filtering: is to see that users are similar to the
pillars for the development of the country, which poses the Mexican active user and then recommend those items that have not been
government tourism policies which might involve the regional rated by the active user and have been well appreciated by similar
improvement. Under the order of the regional, the value of tourism users.
results in the vocation of the destination to be a component that
affects the dynamics and transformation not only of the local 4. Hybrid Filter: Mix one of the two aforementioned filtered
economy, but in respecting the cultural and historical heritage, to make recommendations and even combine it with some artificial
giving spaces reaffirmation of identity and reconstruction of the intelligence technique can be fuzzy logic and evolutionary
social fabric [15]. computation.
In this sense Ciudad Juarez, Chihuahua, has been considered within
44 destinations in tourism development in Mexico, since it is a
border town that is characterized by trafficking with El Paso, Texas
and surrounding areas bidirectionally. The city has positioned itself
as one of the main borders of Mexico thanks to trade, the Consulate
of the United States, hospital trusts, the maquiladora industry
(Cuevas-Contreras, 2010), and not forgetting that frequently hosts
international events, receiving tourists of this kind.
Tourists when they travel to Ciudad Juarez, have a degree of
ignorance about the tourist information offered in the city, which is
one of the limitations to enjoy the ride, making your visit to the city
is not fully satisfactory [3]. So when travelers use the Internet as Figure 1. Schematic of basic operation of a recommender
their main source of information for planning your trip [8]. system
However, the great tourist information you can find about this
destination visited, makes it harder to stay in the City, as not how Examples of major global companies using recommender systems
to make a good choice of activities to be carried out in accordance are given, as in the case of:
with their interests or how you want to enjoy your stay in fate, here
comes a problem for tourists. Amazon is a store that recommends items that might be interesting
As mentioned above, the tourism sector must be able to store and to buy, with a variety of things like technology, books, cooking,
manage all the information generated by their customers in real sports, among others, Amazon recommends that the user is
time, anticipating their expectations, avoiding problems during interested in, and then recommends things related to it who wants
their stay and making the destination a unique experience [6]. to buy, saying "maybe I can interest take these things" or
Adapting to the technological changes that are about to happen, the "Customers who bought this also bought" when he gives these
tourism industry can satisfy an increasingly demanding clientele. options, in the first case uses the flirtation by content because the
system is taking into account what you are buying and the things
3. DESCRIPTION OF THE that can be complementary to it, in the second case, is using a
RECOMMENDATION SYSTEM collaborative approach because it is looking for users who took
certain things when choosing certain item, then as they liked the
3.1 Recommendation Systems system thinks you too may be interested.
Today with technological advancement in computer systems is
more common that tourists seek information before making a trip, YouTube recommends video, if the user makes choices of a certain
or stay about the destination, this accordingly familiarity with using genre of films or music after you enter the main page a series of
the internet, as part of your lifestyle [5]. recommendation from videos that says "recommended for you"
appear, recommend related to what video the genre that you you
In recent years the artificial intelligence community has developed like, use a content-based approach.
an intense work around the recommender systems. These systems
help people find what they especially need on the web and have Recommendation problems are complex and varied as they require
been widely accepted among users [1]. Figure 1 shows the basic a high knowledge of the tastes and preferences of the user to
operation of a recommender system shown, the goal of these agents provide recommendations that are satisfactory [10]. Also they are
is to explore and filter the best options from a user profile considered of great interest both in the scientific aspect as applied.
considering a number of different possibilities, many of them from Therefore, the development of techniques for efficient and
the Web. This involves the construction of a model or user profile adaptable recommendation in different application domains is the
which can be obtained implicitly or explicitly. A detailed taxonomy goal of many research papers.
of recommender systems can be seen in "A taxonomy of No matter how much technology applied to a recommender system,
recommender agents on the Internet" [4] and the main techniques it is important that this complies with the main objective, which
for development can be grouped into the intelligent system [12]. always will: guide the user to the resource most to your preference
1. Content-based filtering: The recommendations are based on or necessity [9] suits.
the knowledge we have about the items that the user has valued and
will recommend similar items that may like it or interest. 3.2 Recommendation Desk Systems
As mentioned above, recommender systems are increasingly used
2. Demographic Filtering: These recommendations are made in many domains. Therefore, they are research topics of various
based on the user characteristics (age, sex, geographic location, projects in the area of artificial intelligence and this research will
profession, etc.) focus on the tourism sector of Ciudad Juarez.
In the field of tourism, recommender systems try to emulate the 4. METHODOLOGY
interactivity of users with tour agents, making personalized For the design of a tourist recommender system in Ciudad Juarez,
recommendations that suit their needs, interests and preferences, it is intended to apply the following methodology, which was based
using the knowledge of the tourist area [11]. on the literature search architecture recommender systems in other
A recent example is the system of tourist recommendation based on fields domain, and analysis of various elements.
artificial intelligence techniques, called troovel.com, developed by
the Research Group Information Technology and Artificial
Intelligence at the Polytechnic University of Valencia [GTI-IA
UPV], which analyzes the interaction between the user and the
application itself and depending on type preferences and similar
tastes of other users, recommended places to visit with detailed
information about each of them, this system has a comprehensive
database that allows offer recommendations in several countries, Figure 3 Methodology proposal for the implementation of a
also adds recommendations initially chords that can surprise the system of tourist recommendation
user [14]. Referencing the figure above, the project follows arises:
Activity 1 - Collect information, tourism resources with which
account Ciudad Juarez, there is no defined and generally accepted
method for inventorying resources, depending on the method of the
place in question and the resources themselves. (Boullon, Roberto)
Activity 2 - Integration of information, creating an index file for
classifying each of tourism resources.
Activity 3 -Store information in a repository database to provide it
to the user when it is requested, depending on their profile.
Activity 4 - Identification of the most appropriate to use in the
system recommendation, to solve the problem of user technical
Figure 2 troovel.com
Activity 5 - Generation User Profile
Basically, tourism involves choosing both the destination to visit,
Activity 6. Conduct a computational algorithm recommender.
the means of transport to be used, the activities to be performed, the
housing is to be used, etc. This occurs because tourism is an activity Once developed the proposal design recommender tourism system
that we are not doing every day and that has a limited duration. [2]. in Ciudad Juarez, allow a number of possibilities for future
Because this, tourists expect to make the best choice for your developments and investigations continue, that allow the
vacation, and what better than a recommendation system to advise implementation of innovative tools as a tourist recommender
them what the best activities in the city, the most interesting visits system, improving management same for both the city and for
for tourists or the best restaurants, all tailored to the preferences of tourists, as the sector will empower and enrich the tourist
each user [2]. experience during their stay according at the differences of each
user and their interests and hobbies, as is shown in figure 4.
In this study we describe a number of methods of recommendation
are commonly used in Recommendation Systems, but we must bear
in mind that they are not mutually exclusive to each other, but
complementary, ie, in the same Recommendation Systems we may
use one or several of these methods. Principle enunciated in the
three simple methods:
Pure recovery or no recommendation, the system offers users a
search interface through which they can make queries to a database
of items. It is, therefore, a search system so technically is not a
recommendation method, although it appears to users as such.
Other systems use manually selected recommendations by experts,
such as publishers, artists or critical recommendations in the case
of movies or music tracks. Experts identify items based on their
own preferences, interests or objectives, and create a list of items
that are available to all system users. Often these recommendations
accompanying text comments that can help users evaluate and
understand the recommendation.
In other cases, the systems provide statistical summaries calculated
based on the views of all users, so they are personalized either. For
example, you could have the percentage of users who have satisfied
or have purchased an item, number of users recommend an item, or
an average evaluation of all users regarding the item into account.
Figure 4. Graphical User Interface of Intelligent sado_en_Conocimiento_para_Recomendacion_de_Informacion_T
Recommender System. uristica_Venezolana
[12] Ochoa, A. (2009). Musical Recommendation on Thematic
5. CONCLUSIONS Web Radio, 4(8), 742–746. Retrieved from
XXI century tourists are increasingly demanding information
http://www.academypublisher.com/jcp/vol04/no08/jcp040874274
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