Call for Papers
Andi SMART | Cristina MOSCONI | Pikakshi MANCHANDA
(University of Exeter, UK)
Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Predictive Modelling, Data Analytics, Computer Vision, Behavioural Analysis, Natural Language Processing
Call: The application of AI approaches is increasing rapidly in the Tourism sector over the past few years. Whilst digital conservation and data collection using AI is well known, the latter can also provide affordances for Cultural Heritage (CH) sites by analysis of visitor profiles, behaviours and feedback.
Obtaining intelligence on visitor motivations and behaviours is an essential activity in the creation and improvement of visitor experiences. Approaches like Machine Learning (ML), Deep Learning (DL) and remote tracking, allow processing of large volumes of data on a scale previously not possible, as well as the development of predictive models to forecast future visitor numbers and revenues based on predictors such as visitor profiles, feedback frequency, weather forecasts etc.
Thus, AI solutions can offer interesting opportunities for CH sites to gain a comprehensive picture of their visitor profiles and experience, and inform existing business models to assess which aspects require more attention.
Contributions are sought for case-based approaches in the cultural heritage sector, investigating the main issues faced, and opportunities encountered, when exploring the implementation of AI technologies. Research on approaches for implementing changes to establish practices are encouraged. Paper analysing innovative tools and methodologies, are also welcome.
Key themes may include:
- Visitor Behavioural Analysis – Use of advanced data collection, analysis and visualisation approaches to extract and represent meaningful insights from visitor data;
- GIS – use of ML/DL and Computer Vision approaches to track visitor journeys and understand visitors’ dwell times at areas of interest;
- Natural Language Processing (NLP) – analysis of visitor feedback (textual/speech) in the form of natural language. Research in the form of visitor support applications such as virtual assistants, chatbots etc is also welcome;
- Predictive Modelling – use of historical visitor data in terms of key predictors such as price, staff interactions etc. to forecast visitor satisfaction and loyalty trends.
Submission (open April 15, 2020)
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