Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research

This paper reviews the published articles on eTourism in the past 20 years. Using a wide variety of sources, mainly in the tourism literature, this paper comprehensively reviews and analyzes prior studies in the context of Internet applications to Tourism. The paper also projects future developments in eTourism and demonstrates critical changes that will influence the tourism industry structure. A major contribution of this paper is its overview of the research and development efforts that have been endeavoured in the field, and the challenges that tourism researchers are, and will be, facing

AgroSupportAnalytics: big data recommender system for agricultural farmer complaints in Egypt

The world’s agricultural needs are growing with the pace of increase in its population. Agricultural farmers play a vital role in our society by helping us in fulfilling our basic food needs. So, we need to support farmers to keep up their great work, even in difficult times such as the coronavirus disease (COVID-19) outbreak, which causes hard regulations like lockdowns, curfews, and social distancing procedures. In this article, we propose the development of a recommender system that assists in giving advice, support, and solutions for the farmers’ agricultural related complaints (or queries). The proposed system is based on the latent semantic analysis (LSA) approach to find the key semantic features of words used in agricultural complaints and their solutions. Further, it proposes to use the support vector machine (SVM) algorithm with Hadoop to classify the large agriculture dataset over Map/Reduce framework. The results show that a semantic-based classification system and filtering methods can improve the recommender system. Our proposed system outperformed the existing interest recommendation models with an accuracy of 87%

Presumptuous aim attribution, conformity, and the ethics of artificial social cognition

Imagine you are casually browsing an online bookstore, looking for an interesting novel. Suppose the store predicts you will want to buy a particular novel: the one most chosen by people of your same age, gender, location, and occupational status. The store recommends the book, it appeals to you, and so you choose it. Central to this scenario is an automated prediction of what you desire. This article raises moral concerns about such predictions. More generally, this article examines the ethics of artificial social cognition—the ethical dimensions of attribution of mental states to humans by artificial systems. The focus is presumptuous aim attributions, which are defined here as aim attributions based crucially on the premise that the person in question will have aims like superficially similar people. Several everyday examples demonstrate that this sort of presumptuousness is already a familiar moral concern. The scope of this moral concern is extended by new technologies. In particular, recommender systems based on collaborative filtering are now commonly used to automatically recommend products and information to humans. Examination of these systems demonstrates that they naturally attribute aims presumptuously. This article presents two reservations about the widespread adoption of such systems. First, the severity of our antecedent moral concern about presumptuousness increases when aim attribution processes are automated and accelerated. Second, a foreseeable consequence of reliance on these systems is an unwarranted inducement of interpersonal conformity

Collaborative-demographic hybrid for financial: product recommendation

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM processes, several financial institutions are striving to leverage customer data and integrate insights regarding customer behaviour, needs, and preferences into their marketing approach. As decision support systems assisting marketing and commercial efforts, Recommender Systems applied to the financial domain have been gaining increased attention. This thesis studies a Collaborative- Demographic Hybrid Recommendation System, applied to the financial services sector, based on real data provided by a Portuguese private commercial bank. This work establishes a framework to support account managers’ advice on which financial product is most suitable for each of the bank’s corporate clients. The recommendation problem is further developed by conducting a performance comparison for both multi-output regression and multiclass classification prediction approaches. Experimental results indicate that multiclass architectures are better suited for the prediction task, outperforming alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study provides important contributions for positioning the bank’s commercial efforts around customers’ future requirements. By allowing for a better understanding of customers’ needs and preferences, the proposed Recommender allows for more personalized and targeted marketing contacts, leading to higher conversion rates, corporate profitability, and customer satisfaction and loyalty

AgroSupportAnalytics: Big data recommender system for agricultural farmer complaints in Egypt

The world's agricultural needs are growing with the pace of increase in its population. Agricultural farmers play a vital role in our society by helping us in fulfilling our basic food needs. So, we need to support farmers to keep up their great work, even in difficult times such as the coronavirus disease (COVID-19) outbreak, which causes hard regulations like lockdowns, curfews, and social distancing procedures. In this article, we propose the development of a recommender system that assists in giving advice, support, and solutions for the farmers' agricultural related complaints (or queries). The proposed system is based on the latent semantic analysis (LSA) approach to find the key semantic features of words used in agricultural complaints and their solutions. Further, it proposes to use the support vector machine (SVM) algorithm with Hadoop to classify the large agriculture dataset over Map/Reduce framework. The results show that a semantic-based classification system and filtering methods can improve the recommender system. Our proposed system outperformed the existing interest recommendation models with an accuracy of 87%

The interplay between food knowledge, nudges, and preference elicitation methods determines the evaluation of a recipe recommender system

Domain knowledge can affect how a user evaluates different aspects of a recommender system. Recipe recommendations might be difficult to understand, as some health aspects are implicit. The appropriateness of a recommender’s preference elicitation (PE) method, whether users rate individual items or item attributes, may depend on the user’s knowledge level. We present an online recipe recommender experiment. Users (𝑁=360) with varying levels of subjective food knowledge faced different cognitive digital nudges (i.e., food labels) and PE methods. In a 3 (recipes annotated with no labels, Multiple Traffic Light (MTL) labels, or full nutrition labels) x2 (PE method : content-based PE or knowledge-based) between-subjects design. We observed a main effect of knowledge-based PE on the healthiness of chosen recipes, while MTL label only helped marginally. A Structural Equation Model analysis revealed that the interplay between user knowledge and the PE method reduced the perceived effort of using the system and in turn, affected choice difficulty and satisfaction. Moreover, the evaluation of health labels depends on a user’s level of food knowledge. Our findings emphasize the importance of user characteristics in the evaluation of food recommenders and the merit of interface and inter action aspects

Trustworthy advice

Abstract 'If you go to Ferran Adria's restaurant you will have the time of your life!' 'If you study everyday for two hours you will get very good marks next semester.' These are examples of advice. We say an advice has two components: a plan to perform and a goal to achieve. In dynamic logic, an advice could be formalised as: [Pη]G. That is, if η performs plan P, then goal G will necessarily be achieved. An adviser is an entity which provides such advice. An adviser may be an agent, a planner, or a complex recommender system. This paper proposes a novel trust model for assessing the trustworthiness of advice and advisers. It calculates the expectation of an advice's outcome by assessing the probabilities of the advised plan being picked up and performed, and the goal being achieved. These probabilities are learned from an analysis of similar past experiences using tools such as semantic matching and action empowerment. © 2015 Elsevier B.V. All rights reserved.This work is supported by the PRAISE project (funded by the European Commission under the FP7 STREP grant number 318770), and the Agreement Technologies project (funded by CONSOLIDER CSD 2007-0022, INGENIO 2010).Peer Reviewe

Incomodando consumidores: quando agentes de recomendação não facilitam a nossa vida realmente

Intelligent systems have been used in different types of websites with the intention of creating personalized messages and understanding consumers’ needs more deeply. They are supposed to facilitate the decision-making process, make internet browsing easier and give users a sense of social feeling and personalization. So far, research in the field has focused attention on the positive aspects of using these systems. Little effort has been made, however, to try to recognize and correct situations in which they do not perform so well. This work is the result of an exploratory research destined to understand the broadness of the concept of failure in personalized environments as well as its antecedents and consequents. Based on the critical incident technique, we collected the opinion of 86 subjects in a multicultural environment and used their responses to elaborate a comprehensive framework of recommendation failure considering the different motivations for Internet use. Keywords: personalization, online shopping, recommendation agents, recommendation failure.Sistemas inteligentes têm sido usados em diferentes tipos de websites com a intenção de criar mensagens personalizadas e de entender as necessidades dos consumidores com mais profundidade. Eles supostamente facilitam o processo de tomada de decisão, tornam a navegação mais fácil e dão aos usuários uma sensação de trato social e personalização. Até agora, a pesquisa na área tem focado nos aspectos positivos de usar tais sistemas. Pouco esforço tem sido feito, entretanto, para tentar reconhecer e corrigir situações nas quais eles não apresentam um bom desempenho. Este trabalho é o resultado de uma pesquisa exploratória destinada a entender a amplitude do conceito de falha em ambientes personalizados, assim como seus antecedentes e consequentes. Baseados na técnica do incidente crítico, coletamos a opinião de 86 participantes em um ambiente multicultural e usamos suas respostas para elaborar um framework abrangente para a falha em recomendação, considerando as diferentes motivações para o uso da internet.Palavras-chave: personalização, compra on-line, agentes de recomendação, falha na recomendação