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

Real-time locating systems (RTLS, also known as real-time location systems) have become an important component of many existing ubiquitous location aware systems. While GPS (global positioning system) has been quite successful as an outdoor real-time locating solution, it fails to repeat this success indoors. A number of RTLS technologies have been used to solve indoor tracking problems. The ability to accurately track the location of assets and individuals indoors has many applications in healthcare. This paper provides a condensed primer of RTLS in healthcare, briefly covering the many options and technologies that are involved, as well as the various possible applications of RTLS in healthcare facilities and their potential benefits, including capital expenditure reduction and workflow and patient throughput improvements. The key to a successful RTLS deployment lies in picking the right RTLS option(s) and solution(s) for the application(s) or problem(s) at hand. Where this application-technology match has not been carefully thought of, any technology will be doomed to failure or to achieving less than optimal results.

/pdf/real-time-locating-systems-rtls-in-healthcare-a-condensed-5le3pjkewr.pdf

Real-time locating systems (RTLS) in healthcare: a condensed primer

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

The School of Creative Arts & Technologies at Ulster University (Magee) has brought together the subject of computing with creative technologies, cinematic arts (film), drama, dance, music and design in terms of research and education. We propose here the development of a flagship computer software platform, SceneMaker, acting as a digital laboratory workbench for integrating and experimenting with the computer processing of new theories and methods in these multidisciplinary fields. We discuss the architecture of SceneMaker and relevant technologies for processing within its component modules. SceneMaker will enable the automated production of multimodal animated scenes from film and drama scripts or screenplays. SceneMaker will highlight affective or emotional content in digital storytelling with particular focus on character body posture, facial expressions, speech, non-speech audio, scene composition, timing, lighting, music and cinematography. Applications of SceneMaker include automated simulation of productions and education and training of actors, screenwriters and directors.

/pdf/scenemaker-creative-technology-for-digital-storytelling-2jrz1jewn6.pdf

SceneMaker: Creative Technology for Digital StoryTelling

In recent years ‘smartphone’ devices are becoming increasingly aware of their surroundings using sensors to detect a user’s current context. Due to this increase in the use of sensors, there is currently a lot of potential for developing context-aware mobile applications. One of the key uses of sensor data in context-aware applications is determining location, as this can greatly improve the potential functionality available to the user. However, there are other types of context that can be determined when developing these applications. For example time, weather and even the person using the device. An activity that can be significantly improved with the introduction of context is mobile tourism. Traditional mobile tourism applications depend heavily on location, and for the most part ignore other types of context. This can lead to the delivery of irrelevant information to the user as there is a lack of adequate content filtering. If the user is inundated with irrelevant or only partially relevant material, this can lead to information overload. Using additional context data can assist these applications in reducing information overload. However, it is important to include an appropriate level of personalisation when performing content filtering to ensure the delivery of key focused information.

Aiden McCaughey

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

SceneMaker: Creative Technology for Digital StoryTelling

VISIT: Virtual Intelligent System for Informing Tourists