Recommender systems are currently being applied in many different domains. This paper focuses on their application in tourism. A comprehensive and thorough search of the smart e-Tourism recommenders reported in the Artificial Intelligence journals and conferences since 2008 has been made. The paper provides a detailed and up-to-date survey of the field, considering the different kinds of interfaces, the diversity of recommendation algorithms, the functionalities offered by these systems and their use of Artificial Intelligence techniques. The survey also provides some guidelines for the construction of tourism recommenders and outlines the most promising areas of work in the field for the next years.

Review: Intelligent tourism recommender systems: A survey

Review: A survey of active and passive indoor localisation systems

Advanced technology enables users to amalgamate information from various sources on their mobile devices, personalise their profile through applications and social networks, as well as interact dynamically with their context. Context-based marketing uses information and communication technologies (ICTs) that recognise the physical environment of their users. Tourism marketers are increasingly becoming aware of those cutting-edge ICTs that provide tools to respond more accurately to the context within and around their users. This paper connects the different concepts of context-based marketing, social media and personalisation, as well as mobile devices. It proposes social context mobile (SoCoMo) marketing as a new framework that enables marketers to increase value for all stakeholders at the destination. Contextual information is increasingly relevant, as big data collected by a wide range of sensors in a smart destination provide real-time information that can influence the tourist experience. SoCoMo marketing introduces a new paradigm for travel and tourism. It enables tourism organisations and destinations to revolutionise their offering and to co-create products and services dynamically with their consumers. The proposed SoCoMo conceptual model explores the emerging opportunities and challenges for all stakeholders.

/pdf/socomo-marketing-for-travel-and-tourism-empowering-co-sunm7sbsxj.pdf

SoCoMo marketing for travel and tourism: Empowering co-creation of value

Emotion recognition using deep learning approach from audio–visual emotional big data

This paper constitutes a literature review on student modeling for the last decade. The review aims at answering three basic questions on student modeling: what to model, how and why. The prevailing student modeling approaches that have been used in the past 10years are described, the aspects of students' characteristics that were taken into consideration are presented and how a student model can be used in order to provide adaptivity and personalisation in computer-based educational software is highlighted. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from e-learning and mobile learning systems to educational games including stand alone educational applications and intelligent tutoring systems. In addition, this paper can be used as a guide for making decisions about the techniques that should be adopted when designing a student model for an adaptive tutoring system. One significant conclusion is that the most preferred technique for representing the student's mastery of knowledge is the overlay approach. Also, stereotyping seems to be ideal for modeling students' learning styles and preferences. Furthermore, affective student modeling has had a rapid growth over the past years, while it has been noticed an increase in the adoption of fuzzy techniques and Bayesian networks in order to deal the uncertainty of student modeling.

Review: Student modeling approaches: A literature review for the last decade

The development of real-time locating systems (RTLS) has become an important add-on to many existing location aware systems. While GPS has solved most of the outdoor RTLS problems, it fails to repeat this success indoors. A number of technologies have been used to address the indoor tracking problem. The ability to accurately track the location of people indoors has many applications ranging from medical, military and logistical to entertainment. However, current systems cannot provide continuous real-time tracking of a moving target or lose capability when coverage is poor. The deployment of a real-time location determination system however is fraught with problems. To date there has been little research into comparing commercial systems on the market with regard to informing IT departments as to their performance in various aspects which are important to tracking devices and people in relatively confined areas. This article attempts to provide such a useful comparison by providing a review of the practicalities of installing certain location-sensing systems. We also comment on the accuracies achieved and problems encountered using the position-sensing systems.

An evaluation of indoor location determination technologies

Increasingly manufacturers of smartphone devices are utilising a diverse range of sensors. This innovation has enabled developers to accurately determine a user's current context. In recent years there has also been a renewed requirement to use more types of context and reduce the current over-reliance on location as a context. Location based systems have enjoyed great success and this context is very important for mobile devices. However, using additional context data such as weather, time, social media sentiment and user preferences can provide a more accurate model of the user's current context. One area that has been significantly improved by the increased use of context in mobile applications is tourism. Traditionally tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions, due to inadequate content filtering and tourists experiencing information overload. These problems can be mitigated if appropriate personalisation and content filtering is performed. The intelligent decision making that this paper proposes with regard to the development of the VISIT [17] system, is a hybrid based recommendation approach made up of collaborative filtering, content based recommendation and demographic profiling. Intelligent reasoning will then be performed as part of this hybrid system to determine the weight/importance of each different context type.

/pdf/context-aware-intelligent-recommendation-system-for-tourism-19xetbp5l7.pdf

Context-aware intelligent recommendation system for tourism

Purpose




Manufacturers of smartphone devices are increasingly utilising a diverse range of sensors. This innovation has enabled developers to accurately determine a user’s current context. One area that has been significantly enhanced by the increased use of context in mobile applications is tourism. Traditionally, tour guide applications rely heavily on location and essentially ignore other types of context. This has led to problems of inappropriate suggestions and tourists experiencing information overload. These problems can be mitigated if appropriate personalisation and content filtering is performed. This research proposes an intelligent context-aware recommender system that aims to minimise the highlighted problems.




Design/methodology/approach




Intelligent reasoning was performed to determine the weight or importance of different types of environmental and temporal context. Environmental context such as the weather outside can have an impact on the suitability of tourist attractions. Temporal context can be the time of day or season; this is particularly important in tourism as it is largely a seasonal activity. Social context such as social media can potentially provide an indication of the “mood” of an attraction. These types of contexts are combined with location data and the context of the user to provide a more effective recommendation to tourists. The evaluation of the system is a user study that utilised both qualitative and quantitative methods, involving 40 participants of differing gender, age group, number of children and marital status.




Findings




This study revealed that the participants selected the context-based recommendation at a significantly higher level than either location-based recommendation or random recommendation. It was clear from analysing the questionnaire results that location is not the only influencing factor when deciding on a tourist attraction to visit.




Research limitations/implications




To effectively determine the success of the recommender system, various combinations of contextual conditions were simulated. Simulating contexts provided the ability to randomly assign different contextual conditions to ensure an effective recommendation under all circumstances. This is not a reflection of the “real world”, because in a “real world” field study the majority of the contextual conditions will be similar. For example, if a tourist visited numerous attractions in one day, then it is likely that the weather conditions would be the same for the majority of the day, especially in the summer season.




Practical implications




Utilising this type of recommender system would allow the tourists to “go their own way” rather than following a prescribed route. By using this system, tourists can co-create their own experience using both social media and mobile technology. This increases the need to retain user preferences and have it available for multiple destinations. The application will be able to learn further through multiple trips, and as a result, the personalisation aspect will be incrementally refined over time. This extensible aspect is increasingly important as personalisation is gradually more effective as more data is collated.




Originality/value




This paper contributes to the body of knowledge that currently exists regarding the study of utilising contextual conditions in mobile recommender systems. The novelty of the system proposed by this research is the combination of various types of temporal, environmental and personal context data to inform a recommendation in an extensible tourism application. Also, performing sentiment analysis on social media data has not previously been integrated into a tourist recommender system. The evaluation concludes that this research provides clear evidence for the benefits of combining social media data with environmental and temporal context to provide an effective recommendation.

/pdf/aggregating-social-media-data-with-temporal-and-mi748qhx6i.pdf

Aggregating social media data with temporal and environmental context for recommendation in a mobile tour guide system

http://www.paulmckevitt.com/pubs/karlaelsevierpaper.pdf

An emotional student model for game-play adaptation

There has been a growth in popularity of privacy in the personal computing space and this has influenced the IT industry. There is more demand for websites to use more secure and privacy focused technologies such as HTTPS and TLS. This has had a knock-on effect of increasing the popularity of Virtual Private Networks (VPNs). There are now more VPN offerings than ever before and some are exceptionally simple to setup. Unfortunately, this ease of use means that businesses will have a need to be able to classify whether an incoming connection to their network is from an original IP address or if it is being proxied through a VPN. A method to classify an incoming connection is to make use of machine learning to learn the general patterns of VPN and non-VPN traffic in order to build a model capable of distinguishing between the two in real time. This paper outlines a framework built on a multilayer perceptron neural network model capable of achieving this goal.

https://pure.ulster.ac.uk/ws/files/76375033/Multilayer_Perceptron_Neural_Network_fo...ction_of_Encrypted_VPN_Network_Traffic.pdf

Tom Lunney

Papers

An evaluation of indoor location determination technologies

Context-aware intelligent recommendation system for tourism

Aggregating social media data with temporal and environmental context for recommendation in a mobile tour guide system

An emotional student model for game-play adaptation

Multilayer Perceptron Neural Network for Detection of Encrypted VPN Network Traffic