Dominance Weighted Social Choice Functions for Group Recommendations

In travel domains, decision support systems provide support to tourists in the planning of their vacation. In particular, when the number of possible Points of Interest (POI) to visit is large, the system should help tourists providing recommendations on the POI that could be more interesting for them. Since traveling is, usually, an activity that involves small groups of people, the system should take simultaneously into account the preferences of each group's member. At the same time, it also should model possible intra-group relationships, which can have an impact in the group decision-making process. In this paper, we model this problem as a multi-agent aggregation of preferences by using weighted social choice functions, whereas such weights are automatically evaluated by analyzing the interactions of the group's members on Online Social Networks

Study of the Relationship between the Number of Travelers and Beach Services in Spain

Spain has traditionally been a country where the coast and beaches have acted as raw material to develop the current tourism industry. This has always led diverse scale works and coastal transformations by Public Administrations in order to satisfy tourist needs and striving to attract a greater number of travelers. However, little and less is known about the objective impact on tourism reached by the final coastal typologies taken as a lot, rather than as a specific singular milestone. Therefore, this work has carried out a study of the 24 coastal provinces of Spain, using 31 variables to analyze the characteristics of 3,470 beaches along 1,905 km of Spanish coastline, to subsequently compare the obtained data with the average number of travelers of each province, in order to search for correlations indexes (R2) that may show tourist behavior patterns according to the variables analyzed. Starting from an initial analysis in absolute terms with low correlation rates, it can be seen that trends vary if Spain is divided into four different geographical areas, North, East, South and islands, showing significantly different correlation rates according to beach types, meteorology, and services in each location. The studied correlations show a great geographical segmentation in the concerns of tourists and may serve as a guide for public and private entities to have objective criteria to establish regeneration typologies in coastal works, based on the desired result concerning tourism and sustainability. It may also reveal that some present and past actions, like regenerating gravel beaches using sand, that pursued this same objective share no relationship with tourism according to this study

Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películas

Providing recommendations to groups of users has become popular in many applications today. Even though there are several group recommendation techniques, the generation of recommendations that satisfy the group members in an even way remains a challenge. Because of this, we have developed a multi-agent approach called MAGReS that relies on negotiation techniques to improve group recommendations. Our approach was tested (on the movies domain) using synthetic data with satisfactory results. Given that the results when using synthetic data may sometimes differ with reality, we decided to assess MAGReS using data from real users. The results obtained showed firstly that, in comparison with the recommendations produced by a traditional approach, the recommendations of MAGReS produce a greater level of satisfaction to the group, and secondly that the proposed approach was able to predict more accurately the satisfaction levels of the group members.Sociedad Argentina de Informática e Investigación Operativ

Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películas

Providing recommendations to groups of users has become popular in many applications today. Even though there are several group recommendation techniques, the generation of recommendations that satisfy the group members in an even way remains a challenge. Because of this, we have developed a multi-agent approach called MAGReS that relies on negotiation techniques to improve group recommendations. Our approach was tested (on the movies domain) using synthetic data with satisfactory results. Given that the results when using synthetic data may sometimes differ with reality, we decided to assess MAGReS using data from real users. The results obtained showed firstly that, in comparison with the recommendations produced by a traditional approach, the recommendations of MAGReS produce a greater level of satisfaction to the group, and secondly that the proposed approach was able to predict more accurately the satisfaction levels of the group members.Sociedad Argentina de Informática e Investigación Operativ

Negociación aplicada a recomendación a grupos: un estudio sobre usuarios en el dominio de películas

Providing recommendations to groups of users has become popular in many applications today. Even though there are several group recommendation techniques, the generation of recommendations that satisfy the group members in an even way remains a challenge. Because of this, we have developed a multi-agent approach called MAGReS that relies on negotiation techniques to improve group recommendations. Our approach was tested (on the movies domain) using synthetic data with satisfactory results. Given that the results when using synthetic data may sometimes differ with reality, we decided to assess MAGReS using data from real users. The results obtained showed firstly that, in comparison with the recommendations produced by a traditional approach, the recommendations of MAGReS produce a greater level of satisfaction to the group, and secondly that the proposed approach was able to predict more accurately the satisfaction levels of the group members.Sociedad Argentina de Informática e Investigación Operativ

Generation of recommendations in an Augmented Reality system applied to tourism based on the context

Actualmente existen aplicaciones orientadas al turismo basadas en realidad aumentada, pero no integran técnicas de recomendación. Este artículo describe RAMCAT (Realidad Aumentada Móvil Contextual Aplicada al Turismo) un modelo de guía turística, que recomienda puntos de interés, teniendo en cuenta factores como preferencias personales y atributos contextuales. Se presentan los componentes teóricos de la arquitectura propuesta, así como sus características, destacando la integración de diferentes sistemas de recomendación, que permiten añadir nuevos motores en el futuro. El artículo se centra en describir sus funcionalidades y el módulo correspondiente al sistema de recomendación basado en el perfil del turista. Otra característica importante del sistema propuesto es la retroalimentación del mismo mediante calificaciones del turista y su trazabilidad.There are many tourist applications using augmented reality, but it’s necessary have models with open architecture to integrate different recommendation techniques. This paper describes RAMCAT, an adaptive tourist guide which recommended points of interest (POIs). Factors such as personal interests and context- -related attributes are important. We present the components of the proposed architecture and its characteristics, emphasizing the integration of different recommender systems, allowing adding new recommendation engine. The article describes with detail, the recommender system based on preferences of tourists. Other characteristic of the system is the feedback through qualifications of tourists and their traceability