Improving cold-start recommendations using item-based stereotypes

Recommender systems (RSs) have become key components driving the success of e-commerce and other platforms where revenue and customer satisfaction is dependent on the user’s ability to discover desirable items in large catalogues. As the number of users and items on a platform grows, the computational complexity and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RSs, while effective in providing suggestions for established users and items, are known for their poor performance for the new user and new item (cold-start) problems. Stereotypical modelling of users and items is a promising approach to solving these problems. A stereotype represents an aggregation of the characteristics of the items or users which can be used to create general user or item classes. We propose a set of methodologies for the automatic generation of stereotypes to address the cold-start problem. The novelty of the proposed approach rests on the findings that stereotypes built independently of the user-to-item ratings improve both recommendation metrics and computational performance during cold-start phases. The resulting RS can be used with any machine learning algorithm as a solver, and the improved performance gains due to rate-agnostic stereotypes are orthogonal to the gains obtained using more sophisticated solvers. The paper describes how such item-based stereotypes can be evaluated via a series of statistical tests prior to being used for recommendation. The proposed approach improves recommendation quality under a variety of metrics and significantly reduces the dimension of the recommendation model

Italy’s “Five Stars” Movement and the role of a leader, or, How charismatic power can resurface through the Web

By focusing on the case of Italian political movement “Five Stars”, founded in 2009 by former comedian Beppe Grillo, this article will analyse the resurfacing of a particular kind of power – the charismatic authority – through a platform such as the Web 2.0 that was expected to promote more rational consensus strategies. Although the political action of the Five Stars movement pretends to be inspired by a participative culture, it is in fact directly ruled by the founder via his blog, with a little space allowed for discussions. In this sense, the rise of Grillo as a political leader seems to both retrieve and renew an old form of authority grounded in a very traditional legitimacy - the charismatic and undisputed leadership of the boss – while at the same time able to spread through the network. This article will offer an overview of the events and also provide a theoretical interpretation

Quality of Experience in Cyber-Physical Social Systems: A Cultural Heritage Space Use Case

In this PhD thesis, the focus is placed on the optimization of user Quality of Experience (QoE) in Cyber Physical Social Systems and speci cally in cultural heritage spaces. In order to achieve maximization of visitor perceived satisfaction, the challenges associated with visitor optimal decision making regarding touring choices and strategies in a museum or a cultural heritage space are examined and the problem of museum congestion is αddressed. Cultural heritage spaces, and museums in particular, constitute a special type of socio-physical system because, in contrast to other social systems like schools or churches, user experience is primarily controlled by the visitors themselves. Such a system also embodies both human behaviors and physical and technical constraints, a fact that makes adopting a socio-technical perspective in order to improve the visiting experience, essential. Within the above setting, quantitative models and functions are initially formulated to express the visitor experience that is gained throughout a touring process. The functions are based on several socio-physical and behavioral factors. Using this QoE modeling approach, the problem of how to optimise visitor route choices is addressed. A social recommendation and personalization framework is also presented that exploits common visitor characteristics and recommends a set of exhibits to be visited. The creation of self-organizing museum visitor communities are proposed as a means to enhance the visiting experience. They exploit visitor personal characteristics and social interactions and are based on a participatory action research (PAR) process. Recommendation Selection and Visiting Time Management (RSVTM) are combined and formulated into a two-stage distributed algorithm, based on game theory and reinforcement learning. In addition, this PhD thesis examines the problem of congestion management in cultural heritage spaces from a more pragmatic perspective, considering visitor behavioral characteristics and risk preferences. The motivation behind this approach arose from the observation that, in cultural heritage spaces, people interact with each other and consequently the decisions and behavior of one visitor influence and are influenced by others. It is, therefore, important to understand the unknown behavior tendencies of visitors especially when making decisions in order to improve their visiting experience and reduce museum congestion. The proposed mechanisms are founded on and powered by the principles of Prospect Theory and the Tragedy of the Commons. Particular attention is paid to modeling and capturing visitor behaviors and decision making under the potential risks and uncertainties which are typically encountered by visitors during their visit. According to their relative popularity and attractiveness, exhibits at a cultural heritage site are classi ed into two main categories: safe exhibits and Common Pool of Resources (CPR) exhibits. CPR exhibits are considered non-excludable and rivalrous in nature, meaning that they may experience "failure" due to over-exploitation. As a result, a visitor's decision to invest time at a CPR exhibit is regarded as risky because his/her perceived satisfaction greatly depends on the cumulative time spent at it by all visitors. A non-cooperative game among the visitors is formulated and solved in a distributed manner in order to determine the optimal investment time at exhibits for each visitor, while maximizing the visitor's perceived satisfaction. Detailed numerical results are presented, which provide useful insights into visitor behaviors and how these influence visitor perceived satisfaction, as well as museum congestion. Finally, pricing is introduced as an effective mechanism to address the problem of museum congestion. Motivated by several studies that position pricing as a mechanism to prevent overcrowding in museums, this thesis analyzes and studies the impact of different pricing policies on visitor decisions when they act as prospect-theoretic decision-makers. The theory of S-modular games is adopted to determine the time invested by each visitor at exhibits while maximizing satisfaction gained

Individuality and the collective in AI agents: Explorations of shared consciousness and digital homunculi in the metaverse for cultural heritage

The confluence of extended reality (XR) technologies, including augmented and virtual reality, with large language models (LLM) marks a significant advancement in the field of digital humanities, opening uncharted avenues for the representation of cultural heritage within the burgeoning metaverse. This paper undertakes an examination of the potentialities and intricacies of such a convergence, focusing particularly on the creation of digital homunculi or changelings. These virtual beings, remarkable for their sentience and individuality, are also part of a collective consciousness, a notion explored through a thematic comparison in science fiction with the Borg and the Changelings in the Star Trek universe. Such a comparison offers a metaphorical framework for discussing complex phenomena such as shared consciousness and individuality, illuminating their bearing on perceptions of self and awareness. Further, the paper considers the ethical implications of these concepts, including potential loss of individuality and the challenges inherent to accurate representation of historical figures and cultures. The latter necessitates collaboration with cultural experts, underscoring the intersectionality of technological innovation and cultural sensitivity. Ultimately, this chapter contributes to a deeper understanding of the technical aspects of integrating large language models with immersive technologies and situates these developments within a nuanced cultural and ethical discourse. By offering a comprehensive overview and proposing clear recommendations, the paper lays the groundwork for future research and development in the application of these technologies within the unique context of cultural heritage representation in the metaverse

Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense

The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server. However, this approach introduces unique communication security challenges that have yet to be thoroughly addressed in the literature. These challenges primarily originate from the decentralized nature of the aggregation process, the varied roles and responsibilities of the participants, and the absence of a central authority to oversee and mitigate threats. Addressing these challenges, this paper first delineates a comprehensive threat model, highlighting the potential risks of DFL communications. In response to these identified risks, this work introduces a security module designed for DFL platforms to counter communication-based attacks. The module combines security techniques such as symmetric and asymmetric encryption with Moving Target Defense (MTD) techniques, including random neighbor selection and IP/port switching. The security module is implemented in a DFL platform called Fedstellar, allowing the deployment and monitoring of the federation. A DFL scenario has been deployed, involving eight physical devices implementing three security configurations: (i) a baseline with no security, (ii) an encrypted configuration, and (iii) a configuration integrating both encryption and MTD techniques. The effectiveness of the security module is validated through experiments with the MNIST dataset and eclipse attacks. The results indicated an average F1 score of 95%, with moderate increases in CPU usage (up to 63.2% +-3.5%) and network traffic (230 MB +-15 MB) under the most secure configuration, mitigating the risks posed by eavesdropping or eclipse attacks

DESIGN FOR BEHAVIOUR CHANGE: A MODEL-DRIVEN APPROACH FOR TAILORING PERSUASIVE TECHNOLOGIES

People generally want to engage in a healthy lifestyle, to live in harmony with the environment, to contribute to social causes, and to avoid behaviours that are harmful for themselves and others. However, people often find it difficult to motivate themselves to engage in these beneficial behaviours. Even adopting a healthy lifestyle, such as healthy eating, physical activity, or smoking cessation, is hard despite being aware of the benefits. The increasing adoption and integration of technologies into our daily lives present unique opportunities to assist individuals to adopt healthy behaviours using technology. As a result, research on how to use technology to motivate health behaviour change has attracted the attention of both researchers and health practitioners. Technology designed for the purpose of bringing about desirable behaviour and attitude changes is referred to as Persuasive Technology (PT). Over the past decade, several PTs have been developed to motivate healthy behaviour, including helping people with addictive behaviour such as substance abuse, assisting individuals to achieve personal wellness, helping people manage diseases, and engaging people in preventive behaviours. Most of these PTs take a one-size-fits-all design approach. However, people differ in their motivation and beliefs about health and what constitutes a healthy life. A technology that motivates one type of person to change her behaviour may actually deter behaviour change for another type of person. As a result, existing PTs that are based on the one-size-fits-all approach may not be effective for promoting healthy behaviour change for most people. Because of the motivational pull that games offer, many PTs deliver their intervention in the form of games. This type of game-based PTs are referred to as persuasive games. Considering the increasing interest in delivering PT as a game, this dissertation uses persuasive games as a case study to illustrate the danger of applying the one-size-fits-all approach, the value and importance of tailoring PT, and to propose an approach for tailoring PTs to increase their efficacy. To address the problem that most existing PTs employ the one-size-fits-all design approach, I developed the Model-driven Persuasive Technology (MPT) design approach for tailoring PTs to various user types. The MPT is based on studying and modelling user’s behaviour with respect to their motivations. I developed the MPT approach in two preliminary studies (N = 221, N = 554) that model the determinants of healthy eating for people from different cultures, of different ages, and of both genders. I then applied the MPT approach in two large-scale studies to develop models for tailoring persuasive games to various gamer types. In the first study (N = 642), I examine eating behaviours and associated determinants, using the Health Belief Model. Using data from the study, I modelled the determinants of healthy eating behaviour for various gamer types. In the second study (N = 1108), I examined the persuasiveness of PT design strategies and developed models for tailoring the strategies to various gamer types. Behavioural determinants and PT design strategies are the two fundamental building blocks that drive PT interventions. The models revealed that some strategies were more effective for particular gamer types, thus, providing guidelines for tailoring persuasive games to various gamer types. To show the feasibility of the MPT design approach, I applied the model to design and develop two versions of a Model-driven Persuasive Game (MPG) targeting two distinct gamer types. To demonstrate the importance of tailoring persuasive games using the MPG approach, I conducted a large-scale evaluation (N = 802) of the two versions of the game and compared the efficacy of the tailored, contra-tailored, and the one-size-fits-all persuasive games condition with respect to their ability to promote positive changes in attitude, self-efficacy, and intention. To also demonstrate that the tailored MPG games inspire better play experience than the one-size-fits-all and the contra-tailored persuasive games, I measure the gamers’ perceived enjoyment and competence under the different game conditions. The results of the evaluation showed that while PTs can be effective for promoting healthy behaviour in terms of attitude, self-efficacy, and intention, the effectiveness of persuasion depends on using the right choice of persuasive strategy for each gamer type. The results showed that one size does not fit all and answered my overarching research question of whether there is a value in tailoring PT to an individual or group. The answer is that persuasive health interventions are more effective if they are tailored to the user types under consideration and that not tailoring PTs could be detrimental to behaviour change

BIBLIOMETRIJSKA ANALIZA UMJETNE INTELIGENCIJE U POSLOVNOJ EKONOMIJI

Invention of artificial intelligence (AI) is certainly one of the most promising technological advancements in modern economy. General AI reaching singularity makes one imagine its disruptive influence. Once invented it is supposed to surpass all human cognitive capabilities. Nevertheless, narrow AI has already been widely applied encompassing many technologies. This paper aims to explore the research area of artificial intelligence with the emphasis on the business economics field. Data has been derived from the records extracted from the Web of Science which is one of the most relevant databases of scientific publications. Total number of extracted records published in the period from 1963-2019 was 1369. Results provide systemic overview of the most influential authors, seminal papers and the most important sources for AI publication. Additionally, using MCA (multiple correspondence analysis) results display the intellectual map of the research field.Otkriće umjetne inteligencije zasigurno predstavlja jednu od najvažniji tehnoloških inovacija moderne ekonomije. Opća umjetna inteligencija koja može dosegnuti singularitet ima potencijal kreirati novu tehnološku arenu. Jednom otkrivena smatra se da će nadmašiti sve ljudske kognitivne sposobnosti. Nadalje, specifična umjetna inteligencija već je otkrivena i primijenjena u brojnim sustavima. Ovaj rad nastoji istražiti područje umjetne inteligencije s naglaskom primjene u ekonomiji. Podaci su derivirani na osnovi zapisa Web of Science baze jednog od najrelevantnijih izvora znanstvenih radova. Ukupan broj ekstrahiranih zapisa u periodu 1963-2019 bio je 1369. Rezultati čine sustavan pregled najutjecajnijih autora, radova te izvora publikacija. Dodatno, koristeći MCA kreirana je intelektualna mapa istraživačkog područja