Context-aware user modeling strategies for journey plan recommendation

Popular journey planning systems, like Google Maps or Yahoo! Maps, usually ignore user’s preferences and context. This paper shows how we applied context-aware recommendation technologies in an existing journey planning mobile application to provide personalized and context-dependent recommendations to users. We describe two different strategies for context-aware user modeling in the journey planning domain. We present an extensive performance comparison of the proposed strategies by conducting a user-centric study in addition to a traditional offline evaluation methodPeer ReviewedPostprint (published version

Predictive Customer Lifetime value modeling: Improving customer engagement and business performance

CookUnity, a meal subscription service, has witnessed substantial annual revenue growth over the past three years. However, this growth has primarily been driven by the acquisition of new users to expand the customer base, rather than an evident increase in customers' spending levels. If it weren't for the raised subscription prices, the company's customer lifetime value (CLV) would have remained the same as it was three years ago. Consequently, the company's leadership recognizes the need to adopt a holistic approach to unlock an enhancement in CLV. The objective of this thesis is to develop a comprehensive understanding of CLV, its implications, and how companies leverage it to inform strategic decisions. Throughout the course of this study, our central focus is to deliver a fully functional and efficient machine learning solution to CookUnity. This solution will possess exceptional predictive capabilities, enabling accurate forecasting of each customer's future CLV. By equipping CookUnity with this powerful tool, our aim is to empower the company to strategically leverage CLV for sustained growth. To achieve this objective, we analyze various methodologies and approaches to CLV analysis, evaluating their applicability and effectiveness within the context of CookUnity. We thoroughly explore available data sources that can serve as predictors of CLV, ensuring the incorporation of the most relevant and meaningful variables in our model. Additionally, we assess different research methodologies to identify the top-performing approach and examine its implications for implementation at CookUnity. By implementing data-driven strategies based on our predictive CLV model, CookUnity will be able to optimize order levels and maximize the lifetime value of its customer base. The outcome of this thesis will be a robust ML solution with remarkable prediction accuracy and practical usability within the company. Furthermore, the insights gained from our research will contribute to a broader understanding of CLV in the subscription-based business context, stimulating further exploration and advancement in this field of study

Advanced recommendations in a mobile tourist information system

An advanced tourist information provider system delivers information regarding sights and events on their users' travel route. In order to give sophisticated personalized information about tourist attractions to their users, the system is required to consider base data which are user preferences defined in their user profiles, user context, sights context, user travel history as well as their feedback given to the sighs they have visited. In addition to sights information, recommendation on sights to the user could also be provided. This project concentrates on combinations of knowledge on recommendation systems and base information given by the users to build a recommendation component in the Tourist Information Provider or TIP system. To accomplish our goal, we not only examine several tourist information systems but also conduct the investigation on recommendation systems. We propose a number of approaches for advanced recommendation models in a tourist information system and select a subset of these for implementation to prove the concept

THOR: A Hybrid Recommender System for the Personalized Travel Experience

One of the travelers’ main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve the problem of information overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid, personalized recommender system for the transportation domain. THOR assigns every traveler a unique contextual preference model built using solely their personal data, which makes the model sensitive to the user’s choices. This model is used to rank travel offers presented to each user according to their personal preferences. We reduce the recommendation problem to one of binary classification that predicts the probability with which the traveler will buy each available travel offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal preference model. Moreover, to tackle the cold start problem for new users, we apply clustering algorithms to identify groups of travelers with similar profiles and build a preference model for each group. To test the system’s performance, we generate a dataset according to some carefully designed rules. The results of the experiments show that the THOR tool is capable of learning the contextual preferences of each traveler and ranks offers starting from those that have the higher probability of being selected

Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems

This position paper summarizes our published review on individual and multistakeholder fairness in Tourism Recommender Systems (TRS). Recently, there has been growing attention to fairness considerations in recommender systems (RS). It has been acknowledged in research that fairness in RS is often closely tied to the presence of multiple stakeholders, such as end users, item providers, and platforms, as it raises concerns for the fair treatment of all parties involved. Hence, fairness in RS is a multi-faceted concept that requires consideration of the perspectives and needs of the different stakeholders to ensure fair outcomes for them. However, there may often be instances where achieving the goals of one stakeholder could conflict with those of another, resulting in trade-offs. In this paper, we emphasized addressing the unique challenges of ensuring fairness in RS within the tourism domain. We aimed to discuss potential strategies for mitigating the aforementioned challenges and examine the applicability of solutions from other domains to tackle fairness issues in tourism. By exploring cross-domain approaches and strategies for incorporating S-Fairness, we can uncover valuable insights and determine how these solutions can be adapted and implemented effectively in the context of tourism to enhance fairness in RS.Comment: Position Paper for FAcctRec 2023 at RecSys 202

Behavioural Change Support Intelligent Transportation Applications

This workshop invites researchers and practitioners to participate in exploring behavioral change support intelligent transportation applications. We welcome submissions that explore intelligent transportation systems (ITS), which interact with travelers in order to persuade them or nudge them towards sustainable transportation behaviors and decisions. Emerging opportunities including the use of data and information generated by ITS and users' mobile devices in order to render personalized, contextualized and timely transport behavioral change interventions are in our focus. We invite submissions and ideas from domains of ITS including, but not limited to, multi-modal journey planners, advanced traveler information systems and in-vehicle systems. The expected outcome will be a deeper understanding of the challenges and future research directions with respect to behavioral change support through ITS.Comment: Intelligent Transportation Systems Conference (ITSC) 2017 Worksho

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

Natural Language based Context Modeling and Reasoning with LLMs: A Tutorial

Large language models (LLMs) have become phenomenally surging, since 2018--two decades after introducing context-awareness into computing systems. Through taking into account the situations of ubiquitous devices, users and the societies, context-aware computing has enabled a wide spectrum of innovative applications, such as assisted living, location-based social network services and so on. To recognize contexts and make decisions for actions accordingly, various artificial intelligence technologies, such as Ontology and OWL, have been adopted as representations for context modeling and reasoning. Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4. In this tutorial, we demonstrate the use of texts, prompts, and autonomous agents (AutoAgents) that enable LLMs to perform context modeling and reasoning without requiring fine-tuning of the model. We organize and introduce works in the related field, and name this computing paradigm as the LLM-driven Context-aware Computing (LCaC). In the LCaC paradigm, users' requests, sensors reading data, and the command to actuators are supposed to be represented as texts. Given the text of users' request and sensor data, the AutoAgent models the context by prompting and sends to the LLM for context reasoning. LLM generates a plan of actions and responds to the AutoAgent, which later follows the action plan to foster context-awareness. To prove the concepts, we use two showcases--(1) operating a mobile z-arm in an apartment for assisted living, and (2) planning a trip and scheduling the itinerary in a context-aware and personalized manner.Comment: Under revie