Multi-Dimensional-Personalization in mobile contexts

During the dot com era the word "personalisation” was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range / short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user’s privacy so that an approach supporting anonymous recommendations through an extended "Chinese Wall“ will be described

A Hybrid Artificial Reputation Model

Agent interaction in a community such as an online buyer-seller scenario is often risky and uncertain. An agent interacts with other agents where initially they know nothing about each other. Currently many reputation models are developed that help consumers select more reputable and reliable service providers. Reputation models also help agents to make a decision on who they should trust and transact with in the future. These reputation models are either built on interaction trust that involves direct experience as a source of information, or they are built upon witness information, also known as word-of-mouth, that involves the reports provided by others. Neither the interaction trust nor the witness information models alone fully succeed in such uncertain interactions. This thesis research introduces the hybrid reputation model combining both interaction trust and witness information to address the shortcomings of existing reputation models when taken separately. Experiments reveal that the hybrid approach leads to better selection of trustworthy agents where consumers select more reputed service providers, eventually lead to more gains by the consumer. Furthermore, the trust model developed is used in calculating trust values of service providers for the case study with a live website ecommerce

O impacto da inteligência artificial no negócio eletrónico

Pela importância que a Inteligência Artificial exibe na atualidade, revela-se de grande interesse verificar até que ponto ela está a transformar o Negócio Eletrónico. Para esse efeito, delineou-se uma revisão sistemática com o objetivo de avaliar os impactos da proliferação destes instrumentos. A investigação empreendida pretendeu identificar artigos científicos que, através de pesquisas realizadas a Fontes de Dados Eletrónicas, pudessem responder às questões de investigação implementadas: a) que tipo de soluções, baseadas na Inteligência Artificial (IA), têm sido usadas para melhorar o Negócio Eletrónico (NE); b) em que domínios do NE a IA foi aplicada; c) qual a taxa de sucesso ou fracasso do projeto. Simultaneamente, tiveram de respeitar critérios de seleção, nomeadamente, estar escritos em inglês, encontrarem-se no intervalo temporal 2015/2021 e tratar-se de estudos empíricos, suportados em dados reais. Após uma avaliação de qualidade final, procedeu-se à extração dos dados pertinentes para a investigação, para formulários criados em MS Excel. Estes dados estiveram na base da análise quantitativa e qualitativa que evidenciaram as descobertas feitas e sobre os quais se procedeu, posteriormente, à sua discussão. A dissertação termina com as conclusão e discussão de trabalhos futuros.Due to the importance that Artificial Intelligence exhibits today, it is of great interest to see to what extent it is transforming the Electronic Business. To this end, a systematic review was designed to evaluate the impacts of the proliferation of these instruments. The research aimed to identify scientific articles that, through research carried out on Electronic Data Sources, could answer the research questions implemented: a) what kind of solutions, based on Artificial Intelligence, have been used to improve the Electronic Business; b) in which areas of the Electronic Business Artificial Intelligence has been applied; c) what the success rate or failure of the project is. At the same time, they must comply with selection criteria, to be written in English, to be found in the 2015/2021-time interval and to be empirical studies supported by actual data. After a final quality evaluation, the relevant data for the investigation were extracted for forms created in MS Excel. These data were the basis of the quantitative and qualitative analysis that evidenced the findings found and on which they were subsequently discussed. The dissertation ends with the conclusion and discussion of future works

Architecture Supporting Computational Trust Formation

Trust is a concept that has been used in computing to support better decision making. For example, trust can be used in access control. Trust can also be used to support service selection. Although certain elements of trust such as reputation has gained widespread acceptance, a general model of trust has so far not seen widespread usage. This is due to the challenges of implementing a general trust model. In this thesis, a middleware based approach is proposed to address the implementation challenges. The thesis proposes a general trust model known as computational trust. Computational trust is based on research in social psychology. An individual’s computational trust is formed with the support of the proposed computational trust architecture. The architecture consists of a middleware and middleware clients. The middleware can be viewed as a representation of the individual that shares its knowledge with all the middleware clients. Each application uses its own middleware client to form computational trust for its decision making needs. Computational trust formation can be adapted to changing circumstances. The thesis also proposed algorithms for computational trust formation. Experiments, evaluations and scenarios are also presented to demonstrate the feasibility of the middleware based approach to computational trust formation

A new technique for intelligent web personal recommendation

Personal recommendation systems nowadays are very important in web applications because of the available huge volume of information on the World Wide Web, and the necessity to save users’ time, and provide appropriate desired information, knowledge, items, etc. The most popular recommendation systems are collaborative filtering systems, which suffer from certain problems such as cold-start, privacy, user identification, and scalability. In this thesis, we suggest a new method to solve the cold start problem taking into consideration the privacy issue. The method is shown to perform very well in comparison with alternative methods, while having better properties regarding user privacy. The cold start problem covers the situation when recommendation systems have not sufficient information about a new user’s preferences (the user cold start problem), as well as the case of newly added items to the system (the item cold start problem), in which case the system will not be able to provide recommendations. Some systems use users’ demographical data as a basis for generating recommendations in such cases (e.g. the Triadic Aspect method), but this solves only the user cold start problem and enforces user’s privacy. Some systems use users’ ’stereotypes’ to generate recommendations, but stereotypes often do not reflect the actual preferences of individual users. While some other systems use user’s ’filterbots’ by injecting pseudo users or bots into the system and consider these as existing ones, but this leads to poor accuracy. We propose the active node method, that uses previous and recent users’ browsing targets and browsing patterns to infer preferences and generate recommendations (node recommendations, in which a single suggestion is given, and batch recommendations, in which a set of possible target nodes are shown to the user at once). We compare the active node method with three alternative methods (Triadic Aspect Method, Naïve Filterbots Method, and MediaScout Stereotype Method), and we used a dataset collected from online web news to generate recommendations based on our method and based on the three alternative methods. We calculated the levels of novelty, coverage, and precision in these experiments, and we found that our method achieves higher levels of novelty in batch recommendation while achieving higher levels of coverage and precision in node recommendations comparing to these alternative methods. Further, we develop a variant of the active node method that incorporates semantic structure elements. A further experimental evaluation with real data and users showed that semantic node recommendation with the active node method achieved higher levels of novelty than nonsemantic node recommendation, and semantic-batch recommendation achieved higher levels of coverage and precision than non-semantic batch recommendation

DIGITAL WINE: HOW PLATFORMS AND ALGORITHMS WILL RESHAPE THE WINE INDUSTRY

La tesi si propone di analizzare come la digitalizzazione e gli approcci basati sui dati, in particolare quelli che sfruttano l'intelligenza artificiale, stiano impattando il settore vitivinicolo e facendo emergere modelli nuovi di business. Quest'ultimo aspetto sarà approfondito tramite due casi studio di piattaforme digitali che, attraverso approcci diversi, stanno contribuendo a generare un ecosistema digitale virtuoso, con potenziali benefici per tutta la catena del valore a livello di settore.The thesis aims to analyze how digitalization and data-driven approaches, in particular those that leverage artificial intelligence, are impacting the wine industry and generating new business models. The latter aspect will be explored through two case studies of digital platforms which, through different approaches, are helping to generate a virtuous digital ecosystem, with potential benefits for the entire value chain at the industry level

Knowledge discovery with recommenders for big data management in science and engineering communities

Recent science and engineering research tasks are increasingly becoming dataintensive and use workflows to automate integration and analysis of voluminous data to test hypotheses. Particularly, bold scientific advances in areas of neuroscience and bioinformatics necessitate access to multiple data archives, heterogeneous software and computing resources, and multi-site interdisciplinary expertise. Datasets are evolving, and new tools are continuously invented for achieving new state-of-the-art performance. Principled cyber and software automation approaches to data-intensive analytics using systematic integration of cyberinfrastructure (CI) technologies and knowledge discovery driven algorithms will significantly enhance research and interdisciplinary collaborations in science and engineering. In this thesis, we demonstrate a novel recommender approach to discover latent knowledge patterns from both the infrastructure perspective (i.e., measurement recommender) and the applications perspective (i.e., topic recommender and scholar recommender). In the infrastructure perspective, we identify and diagnose network-wide anomaly events to address performance bottleneck by proposing a novel measurement recommender scheme. In cases where there is a lack of ground truth in networking performance monitoring (e.g., perfSONAR deployments), it is hard to pinpoint the root-cause analysis in a multi-domain context. To solve this problem, we define a "social plane" concept that relies on recommendation schemes to share diagnosis knowledge or work collaboratively. Our solution makes it easier for network operators and application users to quickly and effectively troubleshoot performance bottlenecks on wide-area network backbones. To evaluate our "measurement recommender", we use both real and synthetic datasets. The results show our measurement recommender scheme has high performance in terms of precision, recall, and accuracy, as well as efficiency in terms of the time taken for large volume measurement trace analysis. In the application perspective, our goal is to shorten time to knowledge discovery and adapt prior domain knowledge for computational and data-intensive communities. To achieve this goal, we design a novel topic recommender that leverages a domain-specific topic model (DSTM) algorithm to help scientists find the relevant tools or datasets for their applications. The DSTM is a probabilistic graphical model that extends the Latent Dirichlet Allocation (LDA) and uses the Markov chain Monte Carlo (MCMC) algorithm to infer latent patterns within a specific domain in an unsupervised manner. We evaluate our scheme based on large collections of the dataset (i.e., publications, tools, datasets) from bioinformatics and neuroscience domains. Our experiments result using the perplexity metric show that our model has better generalization performance within a domain for discovering highly-specific latent topics. Lastly, to enhance the collaborations among scholars to generate new knowledge, it is necessary to identify scholars with their specific research interests or cross-domain expertise. We propose a "ScholarFinder" model to quantify expert knowledge based on publications and funding records using a deep generative model. Our model embeds scholars' knowledge in order to recommend suitable scholars to perform multi-disciplinary tasks. We evaluate our model with state-of-the-art baseline models (e.g., XGBoost, DNN), and experiment results show that our ScholarFinder model outperforms state-ofthe-art models in terms of precision, recall, F1-score, and accuracy.Includes bibliographical references (pages 113-124)

Linking Research and Policy: Assessing a Framework for Organic Agricultural Support in Ireland

This paper links social science research and agricultural policy through an analysis of support for organic agriculture and food. Globally, sales of organic food have experienced 20% annual increases for the past two decades, and represent the fastest growing segment of the grocery market. Although consumer interest has increased, farmers are not keeping up with demand. This is partly due to a lack of political support provided to farmers in their transition from conventional to organic production. Support policies vary by country and in some nations, such as the US, vary by state/province. There have been few attempts to document the types of support currently in place. This research draws on an existing Framework tool to investigate regionally specific and relevant policy support available to organic farmers in Ireland. This exploratory study develops a case study of Ireland within the framework of ten key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, consumer issues, inter-agency activities, and future developments. Data from the Irish Department of Agriculture, Fisheries and Food, the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies provide the basis for an assessment of support in each category. Assessments are based on the number of activities, availability of information to farmers, and attention from governmental personnel for each of the ten categories. This policy framework is a valuable tool for farmers, researchers, state agencies, and citizen groups seeking to document existing types of organic agricultural support and discover policy areas which deserve more attention