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

24th International Conference on Information Modelling and Knowledge Bases

In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok

A field-based computing approach to sensing-driven clustering in robot swarms

Swarm intelligence leverages collective behaviours emerging from interaction and activity of several “simple” agents to solve problems in various environments. One problem of interest in large swarms featuring a variety of sub-goals is swarm clustering, where the individuals of a swarm are assigned or choose to belong to zero or more groups, also called clusters. In this work, we address the sensing-based swarm clustering problem, where clusters are defined based on both the values sensed from the environment and the spatial distribution of the values and the agents. Moreover, we address it in a setting characterised by decentralisation of computation and interaction, and dynamicity of values and mobility of agents. For the solution, we propose to use the field-based computing paradigm, where computation and interaction are expressed in terms of a functional manipulation of fields, distributed and evolving data structures mapping each individual of the system to values over time. We devise a solution to sensing-based swarm clustering leveraging multiple concurrent field computations with limited domain and evaluate the approach experimentally by means of simulations, showing that the programmed swarms form clusters that well reflect the underlying environmental phenomena dynamics

A Machine Learning Approach to Indoor Localization Data Mining

Indoor positioning systems are increasingly commonplace in various environments and produce large quantities of data. They are used in industrial applications, robotics, asset and employee tracking just to name a few use cases. The growing amount of data and the accelerating progress of machine learning opens up many new possibilities for analyzing this data in ways that were not conceivable or relevant before. This paper introduces connected concepts and implementations to answer question how this data can be utilized. Data gathered in this thesis originates from an indoor positioning system deployed in retail environment, but the discussed methods can be applied generally. The issue will be approached by first introducing the concept of machine learning and more generally, artificial intelligence, and how they work on a general level. A deeper dive is done to subfields and algorithms that are relevant to the data mining task at hand. Indoor positioning system basics are also shortly discussed to create a base understanding on the realistic capabilities and constraints that these kinds of systems encase. These methods and previous knowledge from literature are put to test with the freshly gathered data. An algorithm based on existing example from literature was tested and improved upon with the new data. A novel method to cluster and classify movement patterns was introduced, utilizing deep learning to create embedded representations of the trajectories in a more complex learning pipeline. This type of learning is often referred to as deep clustering. The results are promising and both of the methods produce useful high level representations of the complex dataset that can help a human operator to discern the relevant patterns from raw data and to be used as an input for subsequent supervised and unsupervised learning steps. Several factors related to optimizing the learning pipeline, such as regularization were also researched and the results presented as visualizations. The research found that pipeline consisting of CNN-autoencoder followed by a classic clustering algorithm such as DBSCAN produces useful results in the form of trajectory clusters. Regularization such as L1 regression improves this performance. The research done in this paper presents useful algorithms for processing raw, noisy localization data from indoor environments that can be used for further implementations in both industrial applications and academia

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters