There is a large class of Web applications associated with trip planning involving prediction according to user responses and recommendations made in previous trips and associated with travel options required by the tourist (user). This type of innovative applications is called: an intelligent recommendation. In this research, we analyzed a survey instrument presented as the most important examples of trips made in a frontier society. However, for the problem is properly focused, two good examples of a recommendation system can be presented as: 1. Offer travel relators potential tourists and submit it online, based on a prediction of previous recommendations of users with similar profiles associated with the user's interests. 2. Offer tourists associated with different services and products associated with the trip to perform and suggestions online about what they want to buy, based on your purchase history and / or products in searches performed by them in other previous trips. Recommender systems use a number of different technologies. We can classify these systems into two groups (Systems flirtation content and based on the context of the required information systems). This proposed system uses contextual travel information.
Recommender systems; user; planning tourist Ciudad Juarez.
Today tourism is developed in a dynamic and changing context,
which is important to adapt to technological advances, as more and
more tourists frequently rely on them when making a trip [
When tourists travel to any country, region or city for sightseeing,
you want to make the most of your visit and see the greatest
possible number of things and interesting places. If you have
enough time, it is exciting to go slowly and discovering them for
yourself. When time is limited a good guide is essential that advice
and allow them to make a selection (Plaza, 1997). This is where the
Internet use as a means to get tourist information about the place
you visit [
Recommendations systems are tools that generate
recommendations on a particular object of study, from the
preferences and opinions given by users. The use of these systems
is becoming increasingly popular in Internet because they are very
useful to evaluate and filter the vast amount of information
available on the Web in order to assist users in their processes
information retrieval. In this research we will have a review of the
fundamental characteristics and aspects related to the design,
implementation and structure of recommendation systems
analyzing various proposals that have appeared in the literature.
The internet provides a lot of search engines that provides tourist
information, such as; Augmented reality, reservation systems,
ecommerce, web 2.0, big data, among others [
Tourist activity represents 9% of world GDP, Mexico ranks as one
of the ten countries that receives more tourists globally (SECTUR,
2014). Mexico considers the national tourism as one of the four
pillars for the development of the country, which poses the Mexican
government tourism policies which might involve the regional
improvement. Under the order of the regional, the value of tourism
results in the vocation of the destination to be a component that
affects the dynamics and transformation not only of the local
economy, but in respecting the cultural and historical heritage,
giving spaces reaffirmation of identity and reconstruction of the
social fabric [
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 [
As mentioned above, the tourism sector must be able to store and
manage all the information generated by their customers in real
time, anticipating their expectations, avoiding problems during
their stay and making the destination a unique experience [
Today with technological advancement in computer systems is
more common that tourists seek information before making a trip,
or stay about the destination, this accordingly familiarity with using
the internet, as part of your lifestyle [
In recent years the artificial intelligence community has developed
an intense work around the recommender systems. These systems
help people find what they especially need on the web and have
been widely accepted among users [
1. Content-based filtering: The recommendations are based on the knowledge we have about the items that the user has valued and will recommend similar items that may like it or interest.
2. Demographic Filtering: These recommendations are made based on the user characteristics (age, sex, geographic location, profession, etc.) 3. Collaborative filtering: is to see that users are similar to the active user and then recommend those items that have not been rated by the active user and have been well appreciated by similar users.
4. Hybrid Filter: Mix one of the two aforementioned filtered to make recommendations and even combine it with some artificial intelligence technique can be fuzzy logic and evolutionary computation. Examples of major global companies using recommender systems are given, as in the case of: Amazon is a store that recommends items that might be interesting to buy, with a variety of things like technology, books, cooking, sports, among others, Amazon recommends that the user is interested in, and then recommends things related to it who wants to buy, saying "maybe I can interest take these things" or "Customers who bought this also bought" when he gives these options, in the first case uses the flirtation by content because the system is taking into account what you are buying and the things that can be complementary to it, in the second case, is using a collaborative approach because it is looking for users who took certain things when choosing certain item, then as they liked the system thinks you too may be interested.
YouTube recommends video, if the user makes choices of a certain genre of films or music after you enter the main page a series of recommendation from videos that says "recommended for you" appear, recommend related to what video the genre that you you like, use a content-based approach.
Recommendation problems are complex and varied as they require
a high knowledge of the tastes and preferences of the user to
provide recommendations that are satisfactory [
No matter how much technology applied to a recommender system,
it is important that this complies with the main objective, which
always will: guide the user to the resource most to your preference
or necessity [
As mentioned above, recommender systems are increasingly used in many domains. Therefore, they are research topics of various projects in the area of artificial intelligence and this research will focus on the tourism sector of Ciudad Juarez.
In the field of tourism, recommender systems try to emulate the
interactivity of users with tour agents, making personalized
recommendations that suit their needs, interests and preferences,
using the knowledge of the tourist area [
A recent example is the system of tourist recommendation based on
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,
also adds recommendations initially chords that can surprise the
user [
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.
For the design of a tourist recommender system in Ciudad Juarez, it is intended to apply the following methodology, which was based on the literature search architecture recommender systems in other fields domain, and analysis of various elements.
Figure 3 Methodology proposal for the implementation of a system of tourist recommendation
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
Activity 6. Conduct a computational algorithm recommender. Once developed the proposal design recommender tourism system in Ciudad Juarez, allow a number of possibilities for future developments and investigations continue, that allow the implementation of innovative tools as a tourist recommender system, improving management same for both the city and for tourists, as the sector will empower and enrich the tourist experience during their stay according at the differences of each user and their interests and hobbies, as is shown in figure 4.
XXI century tourists are increasingly demanding information tailored to their preferences, continuous development and especially fast new technologies, tourism help to offer new experiences and new tools to improve the development of tourism. Recommender systems are a tool very important decision support in any field, therefore their development should be a thorough process that defines well what kind of information will the items to recommend, so that the quality of the recommendation satisfies the greatest extent possible user need and look filled their expectations. In designing the tourism recommendation system, a lifting inventory of tourism resources will be made, based on the planning method Roberto Boullon. The research presented here aims in future work this application as a tool that offers personalized offers and even can anticipate that customer own demands increasingly informed and increasingly demanding. sado_en_Conocimiento_para_Recomendacion_de_Informacion_T uristica_Venezolana