Personalization in cultural heritage: the road travelled and the one ahead

Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed

A Personalized Travel Recommendation System Using Social Media Analysis

Personalization of recommender systems enables customized services to users. Social media is one resource that aids personalization. This study explores the use of twitter data to personalize travel recommendations. A machine learning classification model is used to identify travel related tweets. The travel tweets are then used to personalize recommendations regarding places of interest for the user. Places of interest are categorized as: historical buildings, museums, parks, and restaurants. To better personalize the model, travel tweets of the user\u27s friends and followers are also mined. Volunteer twitter users were asked to provide their twitter handle as well as rank their travel category preferences in a survey. We evaluated our model by comparing the predictions made by our model with the users choices in the survey. The evaluations show 68% prediction accuracy. The accuracy can be improved with a better travel-tweet training dataset as well as a better travel category identification technique using machine learning. The travel categories can be increased to include items like sports venues, musical events, entertainment, etc. and thereby fine-tune the recommendations. The proposed model lists \u27n\u27 places of interest from each category in proportion to the travel category score generated by the model

Capturing the Visitor Profile for a Personalized Mobile Museum Experience: an Indirect Approach

An increasing number of museums and cultural institutions around the world use personalized, mostly mobile, museum guides to enhance visitor experiences. However since a typical museum visit may last a few minutes and visitors might only visit once, the personalization processes need to be quick and efficient, ensuring the engagement of the visitor. In this paper we investigate the use of indirect profiling methods through a visitor quiz, in order to provide the visitor with specific museum content. Building on our experience of a first study aimed at the design, implementation and user testing of a short quiz version at the Acropolis Museum, a second parallel study was devised. This paper introduces this research, which collected and analyzed data from two environments: the Acropolis Museum and social media (i.e. Facebook). Key profiling issues are identified, results are presented, and guidelines towards a generalized approach for the profiling needs of cultural institutions are discussed

Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs

In the context of Social TV, the increasing popularity of first and second screen users, interacting and posting content online, illustrates new business opportunities and related technical challenges, in order to enrich user experience on such environments. SAM (Socializing Around Media) project uses Social Media-connected infrastructure to deal with the aforementioned challenges, providing intelligent user context management models and mechanisms capturing social patterns, to apply collaborative filtering techniques and personalized recommendations towards this direction. This paper presents the Context Management mechanism of SAM, running in a Social TV environment to provide smart recommendations for first and second screen content. Work presented is evaluated using real movie rating dataset found online, to validate the SAM's approach in terms of effectiveness as well as efficiency.Comment: In: Wu TT., Gennari R., Huang YM., Xie H., Cao Y. (eds) Emerging Technologies for Education. SETE 201

Beyond cute: exploring user types and design opportunities of virtual reality pet games

Virtual pet games, such as handheld games like Tamagotchi or video games like Petz, provide players with artificial pet companions or entertaining pet-raising simulations. Prior research has found that virtual pets have the potential to promote learning, collaboration, and empathy among users. While virtual reality (VR) has become an increasingly popular game medium, litle is known about users' expectations regarding game avatars, gameplay, and environments for VR-enabled pet games. We surveyed 780 respondents in an online survey and interviewed 30 participants to understand users' motivation, preferences, and game behavior in pet games played on various medium, and their expectations for VR pet games. Based on our findings, we generated three user types that reflect users' preferences and gameplay styles in VR pet games. We use these types to highlight key design opportunities and recommendations for VR pet games

Young children learning with mobile devices: Research on design and implementation

The increasing growth and usage of mobile devices, such as tablets and iPads, by young children has not yet been accompanied by systematic research about the effects they have on children's learning and the conditions that facilitate or hinder learning and engagement. As a result, only few empirically-based guidelines exist to guide parents, educators, and application (app) designers when choosing or designing apps for young children, often leading to non-evidence-based decisions, or the design of apps with little educational value. This symposium aims to bring together researchers from Australia, the UK and USA to discuss what evidence exist about the learning potential of mobile devices and apps for young children and how it could be used to inform relevant stakeholders

Case studies of personalized learning

Deliverable 4.1, Literature review of personalised learning and the Cloud, started with an evaluation and synthesis of the definitions of personalized learning, followed by an analysis of how this is implemented in a method (e-learning vs. i-learning, m-learning and u-learning), learning approach and the appropriate didactic process, based on adapted didactic theories. From this research a list of criteria was created needed to implement personalised learning onto the learner of the future. This list of criteria is the basis for the analysis of all case studies investigated. – as well to the learning process as the learning place. In total 60 case studies (all 59 case studies mentioned in D6.4 Education on the Cloud 2015 + one extra) were analysed. The case studies were compared with the list of criteria, and a score was calculated. As a result, the best examples could be retained. On average most case studies were good on: taking different learning methods into account, interactivity and accessibility and usability of learning materials for everyone. All had a real formal education content, thus aiming at the core-curriculum, valuing previous knowledge, competences, life and work skills, also informal. Also the availability of an instructor / tutor or other network of peers, experts and teachers to guide and support the learning is common. On the other hand, most case studies lack diagnostics tests as well at the start (diagnostic entry test), during the personalized learning trajectory and at the end (assessment at the end). Also most do not include non-formal and informal learning aspects. And the ownership of personalized learning is not in the hands of the learner. Five of the 60 case studies can as a result be considered as very good examples of real personalized learning