Location Aware Product Recommendations

Hoje em dia, um típico catálogo de lojas de retalho engloba milhares de produtos e essa vasta quantidade de produtos dificulta ao utilizador a perceção de todas as opções e suas respetivas especificações sem gastar muito tempo em cada viagem de compras. Para dar a conhecer potenciais produtos ao cliente e simultaneamente favorecer potenciais vendas em loja, os sistemas de recomendação são aplicações que reduzem a informação analisada pelo cliente e ajudam a decidir alternativas, ao explorar outros produtos e categorias que possam ser do seu interesse. Com o vasto conhecimento sobre o cliente que as lojas já possuem, é possível extrair informações como preferências do utilizador, padrões de compras, categorias relacionadas com produtos previamente comprados e, portanto, entender o que pode melhorar a experiência de compra para o cliente. Os sistemas de recomendação podem ser aplicados a qualquer tipo de loja, geralmente sistemas de recomendação tradicionais baseados em filtragem colaborativa ou baseada em conteúdo usam modelos simples. Os context‐aware recommenders tem em conta, não só o histórico de compras do cliente, mas também o contexto em que essas compras foram realizadas e o contexto atual do utilizador alvo ao gerar recomendações. Um contexto possível é a localização do cliente e a posição dos artigos dentro da loja, com esse tipo de informação é possível apresentar melhores recomendações, mais personalizadas e oportunas. O produto final de um sistema de recomendação deve ser considerado um poderoso assistente personalizado que conhece os clientes e todos os produtos da loja sendo capaz de os aconselhar e orientar de acordo com seus gostos e, neste caso, a sua localização durante as suas viagens de compras. Aproveitando a experiência e o know‐how da Fraunhofer AICOS nas áreas de localização interna precisa e recomendação de produtos, essas duas técnicas foram combinadas numa solução inovadora que ajuda a melhorar o planeamento das viagens de compras dos clientes oferecendo aconselhamento antes e durante o seu percurso. Foram explorados sistemas de recomendação com conhecimento de contexto combinados com a extração de padrões periódicos para construir uma aplicação de acompanhamento de compras robusto e um sistema de suporte.Nowadays a typical retail chain store catalog encompasses thousands of products, the sheer quantity of products makes it dicult for the customer to be familiar with all the options and their specificities without spending too much time in each shopping trip. In order to make products known that the customer may be interested, while providing potential store sales, recommendation systems are applied to reduce the information examined by the customer and help him decide alternatives, to explore other products and categories that may please him. With the vast customer knowledge that stores already keep, it is possible to extract information such as preferential products, shopping patterns, product related categories and thus understand what can make a better shopping experience for the customer. Recommendation systems can be applied to any store type, usually traditional recommendation systems based on collaborative or content-based filtering use simple models. Context-aware recommenders take into account not only the customer purchase history but the context in which those purchases were made, and also takes into account the target user current context when generating recommendations. One possible context is the user's location and whereabouts inside the store, with this type of information it is possible and desirable to use it to produce better, more personalized and timely (well-timed) product recommendations. The final product of a recommendation system should be considered as a powerfull personalized assistant who knows the customers and all the products of the store, and during their shopping trips, advises and guides them according to their tastes and in this case their location. Taking advantage of Fraunhofer AICOS previous experience and know-how in the areas of accurate internal location and product recommendation, these two techniques were combined into an innovative solution that helps improve customers planning and shopping trips offering counselling before and during the customer journey. Context-aware recommendation systems was explored combined with periodic and sequential pattern mining in order to build a robust shopping companion app and support system

Can Few Lines of Code Change Society ? Beyond fack-checking and moderation : how recommender systems toxifies social networking sites

As the last few years have seen an increase in online hostility and polarization both, we need to move beyond the fack-checking reflex or the praise for better moderation on social networking sites (SNS) and investigate their impact on social structures and social cohesion. In particular, the role of recommender systems deployed at large scale by digital platforms such as Facebook or Twitter has been overlooked. This paper draws on the literature on cognitive science, digital media, and opinion dynamics to propose a faithful replica of the entanglement between recommender systems, opinion dynamics and users' cognitive biais on SNSs like Twitter that is calibrated over a large scale longitudinal database of tweets from political activists. This model makes it possible to compare the consequences of various recommendation algorithms on the social fabric and to quantify their interaction with some major cognitive bias. In particular, we demonstrate that the recommender systems that seek to solely maximize users' engagement necessarily lead to an overexposure of users to negative content (up to 300\% for some of them), a phenomenon called algorithmic negativity bias, to a polarization of the opinion landscape, and to a concentration of social power in the hands of the most toxic users. The latter are more than twice as numerous in the top 1\% of the most influential users than in the overall population. Overall, our findings highlight the urgency to identify harmful implementations of recommender systems to individuals and society in order better regulate their deployment on systemic SNSs

Simulating social relations in multi-agent systems

Open distributed systems are comprised of a large number of heterogeneous nodes with disparate requirements and objectives, a number of which may not conform to the system specification. This thesis argues that activity in such systems can be regulated by using distributed mechanisms inspired by social science theories regarding similarity /kinship, trust, reputation, recommendation and economics. This makes it possible to create scalable and robust agent societies which can adapt to overcome structural impediments and provide inherent defence against malicious and incompetent action, without detriment to system functionality and performance. In particular this thesis describes: • an agent based simulation and animation platform (PreSage), which offers the agent developer and society designer a suite of powerful tools for creating, simulating and visualising agent societies from both a local and global perspective. • a social information dissemination system (SID) based on principles of self organisation which personalises recommendation and directs information dissemination. • a computational socio-cognitive and economic framework (CScEF) which integrates and extends socio-cognitive theories of trust, reputation and recommendation with basic economic theory. • results from two simulation studies investigating the performance of SID and the CScEF. The results show the production of a generic, reusable and scalable platform for developing and animating agent societies, and its contribution to the community as an open source tool. Secondly specific results, regarding the application of SID and CScEF, show that revealing outcomes of using socio-technical mechanisms to condition agent interactions can be demonstrated and identified by using Presage.Open Acces

Machine Learning Models for Educational Platforms

Scaling up education online and onlife is presenting numerous key challenges, such as hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely. However, thanks to the wider availability of learning-related data and increasingly higher performance computing, Artificial Intelligence has the potential to turn such challenges into an unparalleled opportunity. One of its sub-fields, namely Machine Learning, is enabling machines to receive data and learn for themselves, without being programmed with rules. Bringing this intelligent support to education at large scale has a number of advantages, such as avoiding manual error-prone tasks and reducing the chance that learners do any misconduct. Planning, collecting, developing, and predicting become essential steps to make it concrete into real-world education. This thesis deals with the design, implementation, and evaluation of Machine Learning models in the context of online educational platforms deployed at large scale. Constructing and assessing the performance of intelligent models is a crucial step towards increasing reliability and convenience of such an educational medium. The contributions result in large data sets and high-performing models that capitalize on Natural Language Processing, Human Behavior Mining, and Machine Perception. The model decisions aim to support stakeholders over the instructional pipeline, specifically on content categorization, content recommendation, learners’ identity verification, and learners’ sentiment analysis. Past research in this field often relied on statistical processes hardly applicable at large scale. Through our studies, we explore opportunities and challenges introduced by Machine Learning for the above goals, a relevant and timely topic in literature. Supported by extensive experiments, our work reveals a clear opportunity in combining human and machine sensing for researchers interested in online education. Our findings illustrate the feasibility of designing and assessing Machine Learning models for categorization, recommendation, authentication, and sentiment prediction in this research area. Our results provide guidelines on model motivation, data collection, model design, and analysis techniques concerning the above applicative scenarios. Researchers can use our findings to improve data collection on educational platforms, to reduce bias in data and models, to increase model effectiveness, and to increase the reliability of their models, among others. We expect that this thesis can support the adoption of Machine Learning models in educational platforms even more, strengthening the role of data as a precious asset. The thesis outputs are publicly available at https://www.mirkomarras.com

Design of an E-learning system using semantic information and cloud computing technologies

Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process. We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers. In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy. Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí

INDIVIDUALITY OR CONFORMITY: RECOMMENDATION EXPLOITING COMMUNITY-LEVEL SOCIAL INFLUENCE

With the increasing prevalence of online businesses and social networking services, a huge volume of data about transaction records and social connections between users is accumulated at an unprecedented speed, which enables us to take advantage of electronic word-of-mouth effect embedded in social networks for precision marketing and social recommendations. Different from existing works on social recommendations, our research focuses on discriminating the community-level social influence of different friend groups to enhance the quality of recommendation. To this end, we propose a novel probabilistic topic model integrating community detection with topic discovery to model user behaviors. Based on this model, a recommendation method taking both individual interests and conformity influence into consideration is developed. To evaluate the performance of the proposed model and method, experiments are conducted on two real recommendation applications, and the results demonstrate that the proposed recommendation method exhibits superior performance compared with the state-of-art recommendation methods, and the proposed topic model exhibits good explainablibity of topic semantics and community interests. Furthermore, as some people are more individual interest oriented and some are more conformity oriented demonstrated by the experiments, we explore factors that influence each individual’s conformity tendency, and obtain some meaningful findings