Building an Ontology-Based Framework for Tourism Recommendation Services

The tourism product has an intangible nature in that customers cannot physically evallfate the services on offer until practically experienced. This makes having access to ;credible;"i\nd authentic information about tourism products before the actual experience very valuable. An Ontology being a formal, explicit specification of concepts of a domain provides a viable platform for the development of credible knowledge-based tourism information services. In this paper, we present an approach aimed at enabling assorted intelligent reco=endations services in tourism support systems using ontologies. A suite of tourism ontologies was developed and engaged to enable a prototypical e-tourism system with various knowledge-based reco=endation capabilities. A usability evaluation of the system yields encouraging results as a demonstration of the viability of our approach

A Multi-Criteria Recommender System For Tourism Destination

Today, the transmission of information on tourism through internet has been implemented through several systems, among of them are e-Tourism, tourism virtual reality mapping, tourism reservation system, location-based tourism services and tourism recommender system. Of all those varied systems, tourism recommender system plays awfully vital roles because the system is able to provide any tourism information according to the interest and capability of the tourist. However, the development of the tourism recommender system, in fact, faces some problems, among of them are the complexity of the information contained in the tourism objects, and the difficult information extraction related to the existence of the tourism objects themselves. The information upon tourism objects holds many various aspects in relation to the services which the tourists to be will receive, such as completeness facilities of the tourism objects, easy access to the tourism objects, security guarantee, and so on and so forth. In this study, the methods of weighted sum model (WSM) are implemented to develop recommender system for tourism destination. From analysis result of WSM algorithm obtained characteristic of algorithm to generate tourism object recommendations

Destinations ratings based multi-criteria recommender system for Indonesian halal tourism game

Halal tourism is one of the tourism products that have the prospect of contributing to economic growth in Indonesia. Therefore, the government needs to increase promotions to increase tourist interest in halal tourism destinations in Indonesia. Game is one of the alternative promotional media that can also function as an educational medium for choosing halal tourism that is fun for potential tourists. This study proposes a recommendation system to support knowledge sources in the Indonesian halal tourism game. We use destinations ratings-based multi-criteria recommender system (MCRS) to generate recommendation rankings as a reference for visualizing halal travel for players as potential tourists. This method improves the ability of the conventional tourism recommendation system, which is generally based on a single criterion. In this study, we use eight destinations rating criteria as a reference for calculating the recommender system in the halal tourism game. Each of these criteria is a reference for tourists' assessment of halal tourist destinations in Indonesia. Next, we integrate the cosine-based similarity technique in MCRS to measure the level of similarity between players and previous tourist data sets. This research's testing phase uses the theme of halal tourism destinations in the Batu City area. The test results show that the number and composition of the tourism destinations item rating as input of the recommender system affect the accuracy, precision, recall, and F1 scores. Based on 40 experiments with different tourism destination item rating input configurations, it shows that the average value of accuracy = 0.60, precision = 0.67, recall = 0.64 and F1 score = 0.65

Destinations ratings based multi-criteria recommender system for Indonesian halal tourism game

Halal tourism is one of the tourism products that have the prospect of contributing to economic growth in Indonesia. Therefore, the government needs to increase promotions to increase tourist interest in halal tourism destinations in Indonesia. Game is one of the alternative promotional media that can also function as an educational medium for choosing halal tourism that is fun for potential tourists. This study proposes a recommendation system to support knowledge sources in the Indonesian halal tourism game. We use destinations ratings-based multi-criteria recommender system (MCRS) to generate recommendation rankings as a reference for visualizing halal travel for players as potential tourists. This method improves the ability of the conventional tourism recommendation system, which is generally based on a single criterion. In this study, we use eight destinations rating criteria as a reference for calculating the recommender system in the halal tourism game. Each of these criteria is a reference for tourists' assessment of halal tourist destinations in Indonesia. Next, we integrate the cosine-based similarity technique in MCRS to measure the level of similarity between players and previous tourist data sets. This research's testing phase uses the theme of halal tourism destinations in the Batu City area. The test results show that the number and composition of the tourism destinations item rating as input of the recommender system affect the accuracy, precision, recall, and F1 scores. Based on 40 experiments with different tourism destination item rating input configurations, it shows that the average value of accuracy = 0.60, precision = 0.67, recall = 0.64 and F1 score = 0.65

Development of Context-Aware Recommenders of Sequences of Touristic Activities

En els últims anys, els sistemes de recomanació s'han fet omnipresents a la xarxa. Molts serveis web, inclosa la transmissió de pel·lícules, la cerca web i el comerç electrònic, utilitzen sistemes de recomanació per facilitar la presa de decisions. El turisme és una indústria molt representada a la xarxa. Hi ha diversos serveis web (e.g. TripAdvisor, Yelp) que es beneficien de la integració de sistemes recomanadors per ajudar els turistes a explorar destinacions turístiques. Això ha augmentat la investigació centrada en la millora dels recomanadors turístics per resoldre els principals problemes als quals s'enfronten. Aquesta tesi proposa nous algorismes per a sistemes recomanadors turístics que aprenen les preferències dels turistes a partir dels seus missatges a les xarxes socials per suggerir una seqüència d'activitats turístiques que s'ajustin a diversos contextes i incloguin activitats afins. Per aconseguir-ho, proposem mètodes per identificar els turistes a partir de les seves publicacions a Twitter, identificant les activitats experimentades en aquestes publicacions i perfilant turistes similars en funció dels seus interessos, informació contextual i períodes d'activitat. Aleshores, els perfils d'usuari es combinen amb un algorisme de mineria de regles d'associació per capturar relacions implícites entre els punts d'interès de cada perfil. Finalment, es fa un rànquing de regles i un procés de selecció d'un conjunt d'activitats recomanables. Es va avaluar la precisió de les recomanacions i l'efecte del perfil d'usuari. A més, ordenem el conjunt d'activitats mitjançant un algorisme multi-objectiu per enriquir l'experiència turística. També realitzem una segona fase d'anàlisi dels fluxos turístics a les destinacions que és beneficiós per a les organitzacions de gestió de destinacions, que volen entendre la mobilitat turística. En general, els mètodes i algorismes proposats en aquesta tesi es mostren útils en diversos aspectes dels sistemes de recomanació turística.En los últimos años, los sistemas de recomendación se han vuelto omnipresentes en la web. Muchos servicios web, incluida la transmisión de películas, la búsqueda en la web y el comercio electrónico, utilizan sistemas de recomendación para ayudar a la toma de decisiones. El turismo es una industria altament representada en la web. Hay varios servicios web (e.g. TripAdvisor, Yelp) que se benefician de la inclusión de sistemas recomendadores para ayudar a los turistas a explorar destinos turísticos. Esto ha aumentado la investigación centrada en mejorar los recomendadores turísticos y resolver los principales problemas a los que se enfrentan. Esta tesis propone nuevos algoritmos para sistemas recomendadores turísticos que aprenden las preferencias de los turistas a partir de sus mensajes en redes sociales para sugerir una secuencia de actividades turísticas que se alinean con diversos contextos e incluyen actividades afines. Para lograr esto, proponemos métodos para identificar a los turistas a partir de sus publicaciones en Twitter, identificar las actividades experimentadas en estas publicaciones y perfilar turistas similares en función de sus intereses, contexto información y periodos de actividad. Luego, los perfiles de usuario se combinan con un algoritmo de minería de reglas de asociación para capturar relaciones entre los puntos de interés que aparecen en cada perfil. Finalmente, un proceso de clasificación de reglas y selección de actividades produce un conjunto de actividades recomendables. Se evaluó la precisión de las recomendaciones y el efecto de la elaboración de perfiles de usuario. Ordenamos además el conjunto de actividades utilizando un algoritmo multi-objetivo para enriquecer la experiencia turística. También llevamos a cabo un análisis de los flujos turísticos en los destinos, lo que es beneficioso para las organizaciones de gestión de destinos, que buscan entender la movilidad turística. En general, los métodos y algoritmos propuestos en esta tesis se muestran útiles en varios aspectos de los sistemas de recomendación turística.In recent years, recommender systems have become ubiquitous on the web. Many web services, including movie streaming, web search and e-commerce, use recommender systems to aid human decision-making. Tourism is one industry that is highly represented on the web. There are several web services (e.g. TripAdvisor, Yelp) that benefit from integrating recommender systems to aid tourists in exploring tourism destinations. This has increased research focused on improving tourism recommender systems and solving the main issues they face. This thesis proposes new algorithms for tourism recommender systems that learn tourist preferences from their social media data to suggest a sequence of touristic activities that align with various contexts and include affine activities. To accomplish this, we propose methods for identifying tourists from their frequent Twitter posts, identifying the activities experienced in these posts, and profiling similar tourists based on their interests, contextual information, and activity periods. User profiles are then combined with an association rule mining algorithm for capturing implicit relationships between points of interest apparent in each profile. Finally, a rule ranking and activity selection process produces a set of recommendable activities. The recommendations were evaluated for accuracy and the effect of user profiling. We further order the set of activities using a multi-objective algorithm to enrich the tourist experience. We also carry out a second-stage analysis of tourist flows at destinations which is beneficial to destination management organisations seeking to understand tourist mobility. Overall, the methods and algorithms proposed in this thesis are shown to be useful in various aspects of tourism recommender systems

A multiple criteria route recommendation system

The work to be developed in this dissertation is part of a larger project called Sustainable Tourism Crowding (STC), which motivation is based on two negative impacts caused by the tourism overload that happens, particularly, in the historic neighborhoods of Lisbon. The goal of this dissertation is then to mitigate those problems: reduce the tourist burden of points of interest in a city that, in addition to the degradation of the tourist experience, causes sustainability problems in different aspects (environmental, social and local). Within the scope of this dissertation, the implementation of one component of a recommendation system is the proposed solution. It is based on a multi-criteria algorithm for recommending pedestrian routes that minimize the passage through more crowded places and maximizes the visit to sustainable points of interest. These routes will be personalized for each user, as they consider their explicit preferences (e.g. time, budget, physical effort) and several constraints taken from other microservices that are part of the global system architecture mentioned above (e.g. weather conditions, crowding levels, points of interest, sustainability). We conclude it is possible to develop a microservice that recommend personalized routes and communicate with other microservices that are part of the global system architecture mentioned above. The analysis of the experimental data from the recommendation system, allows us to conclude that it is possible to obtain a more balanced distribution of the tourist visit, by increasing the visit to more sustainable places of interest and avoiding crowded paths.O trabalho a desenvolver nesta dissertação insere-se num projeto de maior dimensão denominado Sustainable Tourism Crowding (STC), cuja motivação assenta, essencialmente, em dois impactos negativos provocados pela sobrecarga turística que se verifica, nomeadamente, nos bairros históricos de Lisboa. O objetivo desta dissertação é, então, mitigar esses problemas: reduzir a sobrecarga turística dos pontos de interesse mais visitados numa cidade que, além da degradação da experiência turística, causa problemas de sustentabilidade em diversos aspetos (ambiental, social e local). No âmbito desta dissertação, a implementação de um componente de um sistema de recomendação é a solução proposta. Baseia-se num algoritmo multicritério de recomendação de percursos pedonais que minimiza a passagem por locais mais apinhados e maximizam a visita a pontos de interesse mais sustentáveis. Essas rotas serão personalizadas para cada utilizador, pois consideram as suas preferências (por exemplo, tempo, orçamento, nível de esforço físico) e várias restrições retiradas de outros microsserviços que fazem parte da arquitetura do sistema global mencionado acima (por exemplo, condições meteorológicas, níveis de apinhamento, pontos de interesse, níveis de sustentabilidade). Concluímos que é possível desenvolver um microsserviço que recomenda rotas personalizadas e que comunica com outros microsserviços que fazem parte da arquitetura global do sistema mencionada acima. A análise dos dados experimentais do sistema de recomendação, permite-nos concluir que é possível obter uma distribuição mais equilibrada da visita turística, aumentando a visita a pontos de interesse mais sustentáveis e evitando percursos mais apinhados

A Software Product Line Approach to Ontology-based Recommendations in E-Tourism Systems

This study tackles two concerns of developers of Tourism Information Systems (TIS). First is the need for more dependable recommendation services due to the intangible nature of the tourism product where it is impossible for customers to physically evaluate the services on offer prior to practical experience. Second is the need to manage dynamic user requirements in tourism due to the advent of new technologies such as the semantic web and mobile computing such that etourism systems (TIS) can evolve proactively with emerging user needs at minimal time and development cost without performance tradeoffs. However, TIS have very predictable characteristics and are functionally identical in most cases with minimal variations which make them attractive for software product line development. The Software Product Line Engineering (SPLE) paradigm enables the strategic and systematic reuse of common core assets in the development of a family of software products that share some degree of commonality in order to realise a significant improvement in the cost and time of development. Hence, this thesis introduces a novel and systematic approach, called Product Line for Ontology-based Tourism Recommendation (PLONTOREC), a special approach focusing on the creation of variants of TIS products within a product line. PLONTOREC tackles the aforementioned problems in an engineering-like way by hybridizing concepts from ontology engineering and software product line engineering. The approach is a systematic process model consisting of product line management, ontology engineering, domain engineering, and application engineering. The unique feature of PLONTOREC is that it allows common TIS product requirements to be defined, commonalities and differences of content in TIS product variants to be planned and limited in advance using a conceptual model, and variant TIS products to be created according to a construction specification. We demonstrated the novelty in this approach using a case study of product line development of e-tourism systems for three countries in the West-African Region of Africa

A tourism recommender agent: From theory to practice

In this paper a multiagent Tourism Recommender System is presented. This system has a multiagent architecture and one of its main agents, The Travel Assistant Agent (T-Agent), is modelled as a graded BDI agent. The graded BDI agent model allows to specify an agent’s architecture able to deal with the environment uncertainty and with graded mental attitudes. We focus on the implementational aspects of the multiagent system and specially on the T-Agent development, going from the theoric agent model to the concrete agent implementation.Red de Universidades con Carreras en Informática (RedUNCI