A Direct Reputation Model for VO Formation

We show that reputation is a basic ingredient in the Virtual Organisation (VO) formation process. Agents can use their experiences gained in direct past interactions to model other’s reputation and deciding on either join a VO or determining who is the most suitable set of partners. Reputation values are computed using a reinforcement learning algorithm, so agents can learn and adapt their reputation models of their partners according to their recent behaviour. Our approach is especially powerful if the agent participates in a VO in which the members can change their behaviour to exploit their partners. The reputation model presented in this paper deals with the questions of deception and fraud that have been ignored in current models of VO formation

The Influence of Online Platforms on Decision-Making Process and Behavioural Traits of International Travelers

Marketing communications in the international travel and hospitality industry greatly depend on online platforms. Various goals can be achieved by using digital technologies: advertising, PR, sales, branding, customer relationship management (CRM), data analytics, and online reputation management (ORM). At the same time, international travelers actively use online channels for various stages of behaviour and decision-making processes, for different purposes: pre-departure information search, evaluation of alternatives, booking and purchasing services, and post-travel actions. Besides, the peculiarities of Web 2.0 give customers wide opportunities to disseminate their reviews concerning a service. It may significantly impact potential travelers’ decisions as opinions of other customers are regarded as more reliable. Hence, digital platforms must be considered one of the central issues in tourism marketing. The importance of the issue has increased in the post-pandemic conditions when competition in the international tourism industry worldwide has moved to a new level. The paper provides a discussion and comprehensive analysis of various aspects of using online platforms for marketing purposes: opportunities, decision-making process under the influence of online platforms, behaviour peculiarities of international travelers, and strategies for using online channels to increase influence efficiency. Recommendations and solutions are presented for managing various online platforms. The conclusion briefly summarizes the issues discussed in the paper

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

Evaluation criteria for trust models with specific reference to prejudice filters

The rapid growth of the Internet has resulted in the desperate need for alternative ways to keep electronic transactions secure while at the same time allowing entities that do not know each other to interact. This has, in turn, led to a wide area of interest in the issues of trust and trust modeling to be used by machines. A large amount of work has already been undertaken in this area in an attempt to transfer the trust and interaction decision making processes onto the machine. However this work has taken a number of different approaches with little to no correlation between various models and no standard set of criteria was even proposed that can be used to evaluate the value of such models. The proposed research chooses to use a detailed literature survey to investigate the current models in existence. This investigation focuses on identifying criteria that are required by trust models. These criteria are grouped into four categories that represent four important concepts to be implemented in some manner by trust models: trust representation, initial trust, trust update and trust evaluation. The process of identifying these criteria has led to a second problem. The trust evaluation process is a detailed undertaking requiring a high processing overhead. This process can either result in a value that allows an agent to trust another to a certain extent or in a distrust value that results in termination of the interaction. The evaluation process required to obtain the distrust value is just as process intensive as the one resulting in determining a level of trust and the constraints that will be placed on an interaction. This raises the question: How do we simplify the trust evaluation process for agents that have a high probability of resulting in a distrust value? This research solves this problem by adding a fifth category to the criteria already identified; namely: prejudice filters. These filters have been identified by the literature study and are tested by means of a prototype implementation that uses a specific scenario in order to test two simulation case studies.Dissertation (MSc)--University of Pretoria, 2008.Computer Scienceunrestricte