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 tailored to their preferences, continuous development and 6.pdf especially fast new technologies, tourism help to offer new experiences and new tools to improve the development of tourism. [13] Parra López, Eduardo; Calero García, F. (2006). Las nuevas tecnologías en el sector turístico. Gestión Y Dirección de Empresas Recommender systems are a tool very important decision support Turísticas, 2, 22. Retrieved from in any field, therefore their development should be a thorough https://www.mhe.es/universidad/economia/8448148878/archivos/ process that defines well what kind of information will the items to general_colaboracion2.pdf recommend, so that the quality of the recommendation satisfies the greatest extent possible user need and look filled their expectations. [14] RUVID. (2015, June 25). 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