Balancing adaptivity and customisation : in search of sustainable personalisation in cultural heritage

Personalisation for cultural heritage aims at delivering to visitors the right stories at the right time. Our endeavour to determine which features to use for adaptation starts from acknowledging what forms of personalisation curators value as most meaningful. Working in collaboration with curators we have explored the different features that must be taken into account: some are related to the content (multiple interpretation layers), others to the context of delivery (where and when), but some are idiosyncratic (“match my mood”, “something that is relevant to my life”). The findings reveal that a sustainable personalization needs to accurately balance: (i) support to curators in customising stories to different visitors; (ii) algorithms for the system to dynamically model aspects of the visit and instantiate the correct behaviour; and (iii) an active role for visitors to choose the type of experience they would like to have today

PowerSearch: Augmenting mobile phone search through personalization

Cell phone has become a fundamental element of people\u27s life. People use it to call each other, browse websites, send text messages, etc. Among all the functionalities, the most important and frequently used is the search functionality. Based on ComScore, in July 2008, Google was estimated to host 235 millions searches per day. However, unlike the search on desktop, the search on cell phone has one critical constrain: battery. Cell phone performing a normal Google search, the battery drains very fast. The reason is that when sending a query to and fetching the results from Google, cell phone keeps communicating to the website through networks such as WiFi and 3G. Yet, due to the limited bandwidth of the network and the large amount of the results, the time of communication will be very long. As a result, the battery dies very quickly. In order to prevent this fast drain of battery, a new program is proposed to personalize the search criteria and fetch the most precise and personalized results, instead of all the results, from the web. Because only a few results are fetched, cell phone will not be communicating with the Internet. Hence, the battery will not die very fast. The program can increase the energy-efficiency of the battery and, thus, lengthen the running time of the cell phone

Specifics of Algorithmization in Data Culture

Information societies effectively transform existing cultures. New cultures are variously defined, but because of the fact that they are dominated by information, the term “data cultures” seems to be the most relevant name for them. Although, it is possible to create a single global data culture in the future and such predictions prevail in academic and non-academic reflections on this subject, so far in addition to global trends there occur local data cultures, what also dynamizes and enriches both individual and collective identities. As Kazimierz Krzysztofek aptly notes, in this situation: "The greatest contradiction of the civilization of the 21st century is drawn. On the one hand, a continuous imperative: be creative and innovative, on the other hand, an increasing pressure on prediction of people’s behaviour, because unpredictability causes chaos, which cannot be managed". In other words, one of the most important social issues today is to create some order in data culture / cultures (often pictured by columnists and researchers as a "magnetic storm"), to reduce its / their infinite complexity, i.e. simply the algorithmization process. With regard to culture, it is not possible to use unequivocally a mathematical algorithm that is the most precise, or a genetic or hormonal algorithm that functions in nature, because accustoming cultural chaos is always strongly ideologized. The algorithm should be treated as a metaphor used to explain cultural phenomena, especially their developmental tendencies. For the researcher of contemporary societies, it is very important to answer the question: what proportions of structure and network are the most beneficial for the survival of data culture / cultures? This answer also directs reflection on the quality of life of individuals and societies, limiting or promoting individualism and collective intelligence in the era of hyper-digitization. These considerations are limited to the initial characterization and evaluation of the information algorithmization of man. The author of the study refers to the concepts of researchers from different countries, highlighting the specificity of today's algorithmization, among others the model of ambient perception, which facilitates participation in the networked information environment, scope and reach of the big data phenomenon, forms of data visualization, personalization of content, Isotype visual language, network custody, data journalism and others. In conclusion, it is pointed out that the information algorithmization of man is constantly growing, which proves that data management strategies weaken the phenomenon of information overload through the logic of numerical civilization, which limits diversity, seeking to count, record and globalize everything

Convergence of Gamification and Machine Learning: A Systematic Literature Review

Recent developments in human–computer interaction technologies raised the attention towards gamification techniques, that can be defined as using game elements in a non-gaming context. Furthermore, advancement in machine learning (ML) methods and its potential to enhance other technologies, resulted in the inception of a new era where ML and gamification are combined. This new direction thrilled us to conduct a systematic literature review in order to investigate the current literature in the field, to explore the convergence of these two technologies, highlighting their influence on one another, and the reported benefits and challenges. The results of the study reflect the various usage of this confluence, mainly in, learning and educational activities, personalizing gamification to the users, behavioral change efforts, adapting the gamification context and optimizing the gamification tasks. Adding to that, data collection for machine learning by gamification technology and teaching machine learning with the help of gamification were identified. Finally, we point out their benefits and challenges towards streamlining future research endeavors.publishedVersio