IMPROVING THE DEPENDABILITY OF DESTINATION RECOMMENDATIONS USING INFORMATION ON SOCIAL ASPECTS

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process

Improving the Dependability of Destination Recommendations using Information on Social Aspects

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process.Content-based filtering; Recommender Systems; Ontology; Social Attributes, Destination recommendation

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

On content-based recommendation and user privacy in social-tagging systems

Recommendation systems and content filtering approaches based on annotations and ratings, essentially rely on users expressing their preferences and interests through their actions, in order to provide personalised content. This activity, in which users engage collectively has been named social tagging, and it is one of the most popular in which users engage online, and although it has opened new possibilities for application interoperability on the semantic web, it is also posing new privacy threats. It, in fact, consists of describing online or offline resources by using free-text labels (i.e. tags), therefore exposing the user profile and activity to privacy attacks. Users, as a result, may wish to adopt a privacy-enhancing strategy in order not to reveal their interests completely. Tag forgery is a privacy enhancing technology consisting of generating tags for categories or resources that do not reflect the user's actual preferences. By modifying their profile, tag forgery may have a negative impact on the quality of the recommendation system, thus protecting user privacy to a certain extent but at the expenses of utility loss. The impact of tag forgery on content-based recommendation is, therefore, investigated in a real-world application scenario where different forgery strategies are evaluated, and the consequent loss in utility is measured and compared.Peer ReviewedPostprint (author’s final draft

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 mobile tour guide app for sustainable tourism

Portugal has had a flourishing tourism sector for the past few years. In fact, Portugal’s tourism boom has made the industry one of the biggest contributors to the national economy and the largest employer. In the year 2019, Portugal had a total of 27 million tourists, surpassing once again the record established in the previous year. However, tourism also brings a series of unintended negative side effects, such as overcrowding. The Santa Maria Maior historic district in Lisbon is being particularly affected by this problem. The work undertaken in this dissertation is part of the Sustainable Tourism Crowding project, that aims to mitigate the overcrowding phenomenon in this district, by fostering a balanced distribution of visitors while promoting the visitation of sustainable points of interest. This dissertation focuses on developing a mobile app prototype targeted at tourists, through which these sustainable walking tour recommendations can be delivered. To validate the functional requirements of the prototype, more specifically the trip creation process, a series of unit tests, integration tests, and manual tests were developed. To evaluate the usability of the prototype, a user-centered approach was adopted during the design stage, in which two usability techniques were conducted with members of ISCTE’s research center ISTAR and partners from the Junta de Freguesia de Santa Maria Maior, that guided and validated the decisions made. The achieved prototype contains mechanisms for measuring tourists’ adherence to the recommended tours using the Dynamic Time Warping algorithm, which raises new research opportunities on tourists’ behaviour.O desenvolvimento próspero do setor turístico em Portugal nos últimos anos fez da indústria um dos maiores contribuintes para a economia nacional e o maior empregador do país. No ano de 2019, Portugal recebeu um total de 27 milhões de turistas, ultrapassando uma vez mais uma vez o recorde estabelecido no ano anterior. No entanto, o turismo traz também uma série de efeitos secundários negativos não intencionais, tais como overcrowding. A freguesia histórica de Santa Maria Maior em Lisboa está a ser particularmente afetada por este problema. O trabalho desenvolvido nesta dissertação faz parte do projeto de pesquisa Sustainable Tourism Crowding, que visa mitigar o fenómeno de overcrowding nesta freguesia, promovendo uma distribuição equilibrada dos visitantes e incentivando a visita de pontos de interesse sustentáveis. Esta dissertação foca-se no desenvolvimento de uma aplicação móvel protótipo destinada a turistas, através do qual recebem recomendações de visitas sustentáveis. Para validar os requisitos funcionais do protótipo, mais especificamente o processo de criação de visitas, foram desenvolvidos testes unitários, testes de integração, e testes manuais. Para avaliar a usabilidade do protótipo, foi adotada uma abordagem centrada no utilizador durante a fase de conceção, em que foram utilizadas duas técnicas de usabilidade em parceria com o ISTAR (centro de investigação do ISCTE) e com a Junta de Freguesia de Santa Maria Maior, cujos resultados guiaram e validaram as decisões tomadas. O protótipo desenvolvido contém mecanismos para medir a aderência dos turistas às recomendações sugeridas através do algoritmo Dynamic Time Warping, proporcionando novas oportunidades de pesquisa nesta área

Evaluating Conversational Recommender Systems: A Landscape of Research

Conversational recommender systems aim to interactively support online users in their information search and decision-making processes in an intuitive way. With the latest advances in voice-controlled devices, natural language processing, and AI in general, such systems received increased attention in recent years. Technically, conversational recommenders are usually complex multi-component applications and often consist of multiple machine learning models and a natural language user interface. Evaluating such a complex system in a holistic way can therefore be challenging, as it requires (i) the assessment of the quality of the different learning components, and (ii) the quality perception of the system as a whole by users. Thus, a mixed methods approach is often required, which may combine objective (computational) and subjective (perception-oriented) evaluation techniques. In this paper, we review common evaluation approaches for conversational recommender systems, identify possible limitations, and outline future directions towards more holistic evaluation practices