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

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

Adaptive Information Visualization for Personalized Access to Educational Digital Libraries

Personalization is one of the emerging ways to increase the power of modern Digital Libraries. The Knowledge Sea II system presented in this paper explores social navigation support, an approach for providing personalized guidance within the open corpus of educational resources. Following the concepts of social navigation we have attempted to organize a personalized navigation support that is based on past learners’ interaction with the system. The study indicates that Knowledge Sea II became the students' primary tool for accessing the open corpus documents used in a programming course. The social navigation support implemented in this system was considered useful by students participating in the study of Knowledge Sea II. At the same time, some user comments indicated the need to provide more powerful navigational support, such as the ability to rank the usefulness of a page

Mining Frequent Generalized Patterns for Web Personalization in the presence of Taxonomies

The Web is a continuously evolving environment, since its content is updated on a regular basis. As a result, the traditional usage-based approach to generate recommendations that takes as input the navigation paths recorded on the Web page level, is not as effective. Moreover, most of the content available online is either explicitly or implicitly characterized by a set of categories organized in a taxonomy, allowing the page-level navigation patterns to be generalized to a higher, aggregate level. In this direction, the authors present the Frequent Generalized Pattern (FGP) algorithm. FGP takes as input the transaction data and a hierarchy of categories and produces generalized association rules that contain transaction items and/or item categories. The results can be used to generate association rules and subsequently recommendations for the users. The algorithm can be applied to the log files of a typical Web site; however, it can be more helpful in a Web 2.0 application, such as a feed aggregator or a digital library mediator, where content is semantically annotated and the taxonomic nature is more complex, requiring us to extend FGP in a version called FGP+. The authors experimentally evaluate both algorithms using Web log data collected from a newspaper Web site

Location-based social media and the strategic impact for companies

In the last couple of years online social networks expanded to a new field, location (Scellato and Mascolo, 2011). Technologies, such as smartphones and GPS, combined with users’ interest in being connected regardless of their location, created the opportunity for the appearance of location-based social media (Chow et al, 2010). This dissertation focuses in studying if location-based social media has a strategic impact for companies. To contextualize this subject, literature on Web 2.0 and online social media is reviewed. Furthermore, strategic frameworks (Resource Based View) and strategic concepts (Customer Relationship Management and Contextual Marketing) provide the theoretical base through which the discussion is carried. Empirical data collection is conducted in order to understand what are users’ preferences in the context of location-based social media, and to what extent they are willing to interact with companies. Through this process the research hypothesis presented in this dissertation are tested. The results are then extended to the strategic domain, allowing to comprehend under what assumptions location-based social media can be strategic for companies. Through the Resource Based View framework application contextual personalization is considered a factor that may conduct companies to obtain a sustained competitive advantage, by inducing switching costs to their customers, depending on companies’ propensity to appropriate returns from their existing superior capabilities. This dissertation concludes that location-based social networks can have a strategic impact for companies, under the assumptions that network effects exist in location-based social networks and that companies are able to use them in order to perform contextual personalization, originating switching costs for their customers. Additionally, this dissertation aims to contribute for the increase of the current knowledge over an emergent and present subject.Nos últimos dois anos as redes sociais online expandiram-se para uma nova área, localização (Scellato and Mascolo, 2011). Tecnologias, como os “smartphones” e GPS, combinadas com o interesse por parte dos utilizadores em estarem conectados, independentemente da sua localização, criaram a oportunidade para o aparecimento das redes sociais de geo-localização (Chow et al, 2010). Esta dissertação foca-se no estudo da existência ou não de impacto estratégico das redes sociais de geo-localização para as empresas. Para contextualizar este assunto, a literatura sobre Web 2.0 e as redes sociais online é revista. Adicionalmente, “frameworks” (“Resource Based View”) e conceitos (“Customer Relationship Management and Contextual Marketing”) estratégicos providenciam a base teórica através da qual a discussão é conduzida. A recolha de dados empíricos é conduzida com o intuito de compreender quais as preferências dos utilizadores das redes sociais de geo-localização, e até que ponto eles estão dispostos a interagir com as empresas. Através deste processo as hipóteses de investigação foram testadas. Os resultados foram posteriormente estendidos ao domínio estratégico, permitindo compreender sob que pressupostos as redes sociais de geo-localização são estratégicas para as empresas. Através da aplicação do “Resource Based View framework” a personalização contextual é considerada um factor que pode conduzir as empresas à obtenção de uma vantagem competitiva sustentada, induzindo custos de mudança aos seus consumidores, dependendo da capacidade das empresas em se apropriarem de retornos gerados pelas suas capacidades superiores existentes. Esta dissertação conclui que as redes sociais de geo-localização podem ter um impacto estratégico para as empresas, de acordo com os pressupostos de que os efeitos de rede existem nas redes sociais de geo-localização e de que as empresas são capazes de realizar personalização contextual através das mesmas, originando custos de mudança para os seus clientes. Adicionalmente, esta dissertação espera contribuir para o aumento do conhecimento actual sobre um tópico emergente e actual

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

The SEArch smart environments architecture

we report on a Smart Environment Architecture (SEArch) which has been developed to support innovative Ambient Assisted Living services. We explain SEArch at a conceptual level and also how it has been linked to a sensing environment. We compare SEArch to other similar systems reported in the technical literature. We illustrate how the system works using a practical automation scenario