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

From Personalization to Adaptivity: Creating Immersive Visits through Interactive Digital Storytelling at the Acropolis Museum

Storytelling has recently become a popular way to guide museum visitors, replacing traditional exhibit-centric descriptions by story-centric cohesive narrations with references to the exhibits and multimedia content. This work presents the fundamental elements of the CHESS project approach, the goal of which is to provide adaptive, personalized, interactive storytelling for museum visits. We shortly present the CHESS project and its background, we detail the proposed storytelling and user models, we describe the provided functionality and we outline the main tools and mechanisms employed. Finally, we present the preliminary results of a recent evaluation study that are informing several directions for future work

Profiling Attitudes for Personalized Information Provision

PAROS is a generic system under design whose goal is to offer personalization, recommendation, and other adaptation services to information providing systems. In its heart lies a rich user model able to capture several diverse aspects of user behavior, interests, preferences, and other attitudes. The user model is instantiated with profiles of users, which are obtained by analyzing and appropriately interpreting potentially arbitrary pieces of user-relevant information coming from diverse sources. These profiles are maintained by the system, updated incrementally as additional data on users becomes available, and used by a variety of information systems to adapt the functionality to the users’ characteristics

Recommendations as Graph Explorations

We argue that most recommendation approaches can be abstracted as a graph exploration problem. In particular, we describe a graph-theoretic framework with two primary parts: (a) a recommendation graph, modeling all the elements of an (application) domain from a recommendation perspective, including the subjects and objects of recommendations as well as the relationships between them; (b) a set of path operations, inferring newedges, i.e., implicit or unknown relationships, by traversing and combining paths on the graph. The resulting path algebra model provides an abstraction and a common foundation that is beneficial to three aspects of recommendations: (a) expressive power - expression and subsequent use of several significantly different, existing but also novel recommendation approaches is reduced to parameterizing a unique model; (b) usability - by capturing part of the recommendation mechanisms in the underlying path algebra semantics, specification of recommendation approaches becomes easier and less tedious; (c) processing speed implementing recommender systems on top of graph engines opens up the door for several optimizations that speed up execution. We demonstrate the above benefits by expressing several categories of recommendation approaches in the path algebra model and benchmarking some of them in a recommender system implemented on top of Neo4J, a widely used graph system

Recommendations for Explorations based on Graphs

Recommendations are an integral part of data exploration. Existing approaches, however, consider a limited model of recommendations. In this vision paper, we lay the ground for a graph-based approach for recommendations that allows significant flexibility in capturing both data and recommendations and process them efficiently. We determine the requirements of a desired solution and illustrate the overall idea with an example based on the Yelp dataset

On Achieving Diversity in Recommender Systems

Throughout our digital lives, we are getting recommendations for about almost everything we do, buy or consume. In that way, the field of recommender systems has been evolving vastly to match the increasing user needs accordingly. News, products, ideas and people are only a few of the things that we can be recommended with daily. However, even with the many years of research, several areas still remain unexplored. The focus of this paper revolves around such an area, namely on how to achieve diversity in single-user and group recommendations. Specifically, we decouple diversity from strictly revolving around items, and consider it as an orthogonal dimension that can be incorporated independently at different times in the recommender's workflow. We consider various definitions of diversity, taking into account either data items or users characteristics, and study how to cope with them, depending on whether we opt at diversity-aware single-user or group recommendations

From personalization to adaptivity - Creating immersive visits throughinteractive digital storytelling at the Acropolis Museum

Storytelling has recently become a popular way to guide museum visitors, replacing traditional exhibit-centric descriptions by story-centric cohesive narrations with references to the exhibits and multimedia content. This work presents the fundamental elements of the CHESS project approach, the goal of which is to provide adaptive, personalized, interactive storytelling for museum visits. We shortly present the CHESS project and its background, we detail the proposed storytelling and user models, we describe the provided functionality and we outline the main tools and mechanisms employed. Finally, we present the preliminary results of a recent evaluation study that are informing several directions for future work

Authoring personalized interactive museum stories

CHESS is a research prototype system aimed at enriching museum visits through personalized interactive storytelling. Aspiring to replace traditional exhibit-centric descriptions by story-centric cohesive narrations with carefully-designed references to the exhibits, CHESS follows a plot-based approach, where the story authors create stories around pre-selected museum themes. In this paper we place the CHESS system within the Interactive Digital Narrative field, describing the main objectives and requirements addressed. We present the system's architecture and outline its overall functionality. We describe the underlying storytelling model using examples from the stories authored using the CHESS Authoring Tool. Finally, we report key results focusing on the authors' perspective for the creation of personalized stories

CHESS: Personalized storytelling experiences in museums

In this work, we present the CHESS research prototype system which offers personalized, interactive digital storytelling experiences to enhance museum visits, demonstrating the authoring and visiting experiences