Predicting workout quality to help coaches support sportspeople

The support of a qualified coach is crucial to keep the motivation of sportspeople high and help them pursuing an active lifestyle. In this paper,we discuss the scenario in which a coach follows sportspeople remotely by means of an eHealth platform, named u4fit. Having to deal with several users at the same time, with no direct human contact, means that it is hard for coaches to quickly spot who, among the people she follows, needs a more timely support. To this end, in this paper we present an automated approach that analyzes the adherence of sportspeople to their planned workout routines. The approach is able to suggest to the coach the sportspeople who need earlier support due to a poor performance. Experiments on real data, evaluated through classic accuracy metrics, show the effectiveness of our approach

User-oriented recommender systems in retail

User satisfaction is considered a key objective for all service provider platforms, regardless of the nature of the service, encompassing domains such as media, entertainment, retail, and information. While the goal of satisfying users is the same across different domains and services, considering domain-specific characteristics is of paramount importance to ensure users have a positive experience with a given system. User interaction data with a system is one of the main sources of data that facilitates achieving this goal. In this thesis, we investigate how to learn from domain-specific user interactions. We focus on recommendation as our main task, and retail as our main domain. We further explore the finance domain and the demand forecasting task as additional directions to understand whether our methodology and findings generalize to other tasks and domains. The research in this thesis is organized around the following dimensions: 1) Characteristics of multi-channel retail: we consider a retail setting where interaction data comes from both digital (i.e., online) and in-store (i.e., offline) shopping; 2) From user behavior to recommendation: we conduct extensive descriptive studies on user interaction log datasets that inform the design of recommender systems in two domains, retail and finance. Our key contributions in characterizing multi-channel retail are two-fold. First, we propose a neural model that makes use of sales in multiple shopping channels in order to improve the performance of demand forecasting in a target channel. Second, we provide the first study of user behavior in a multi-channel retail setting, which results in insights about the channel-specific properties of user behavior, and their effects on the performance of recommender systems. We make three main contributions in designing user-oriented recommender systems. First, we provide a large-scale user behavior study in the finance domain, targeted at understanding financial information seeking behavior in user interactions with company filings. We then propose domain-specific user-oriented filing recommender systems that are informed by the findings of the user behavior analysis. Second, we analyze repurchasing behavior in retail, specifically in the grocery shopping domain. We then propose a repeat consumption-aware neural recommender for this domain. Third, we focus on scalable recommendation in retail and propose an efficient recommender system that explicitly models users' personal preferences that are reflected in their purchasing history

User-oriented recommender systems in retail

User satisfaction is considered a key objective for all service provider platforms, regardless of the nature of the service, encompassing domains such as media, entertainment, retail, and information. While the goal of satisfying users is the same across different domains and services, considering domain-specific characteristics is of paramount importance to ensure users have a positive experience with a given system. User interaction data with a system is one of the main sources of data that facilitates achieving this goal. In this thesis, we investigate how to learn from domain-specific user interactions. We focus on recommendation as our main task, and retail as our main domain. We further explore the finance domain and the demand forecasting task as additional directions to understand whether our methodology and findings generalize to other tasks and domains. The research in this thesis is organized around the following dimensions: 1) Characteristics of multi-channel retail: we consider a retail setting where interaction data comes from both digital (i.e., online) and in-store (i.e., offline) shopping; 2) From user behavior to recommendation: we conduct extensive descriptive studies on user interaction log datasets that inform the design of recommender systems in two domains, retail and finance. Our key contributions in characterizing multi-channel retail are two-fold. First, we propose a neural model that makes use of sales in multiple shopping channels in order to improve the performance of demand forecasting in a target channel. Second, we provide the first study of user behavior in a multi-channel retail setting, which results in insights about the channel-specific properties of user behavior, and their effects on the performance of recommender systems. We make three main contributions in designing user-oriented recommender systems. First, we provide a large-scale user behavior study in the finance domain, targeted at understanding financial information seeking behavior in user interactions with company filings. We then propose domain-specific user-oriented filing recommender systems that are informed by the findings of the user behavior analysis. Second, we analyze repurchasing behavior in retail, specifically in the grocery shopping domain. We then propose a repeat consumption-aware neural recommender for this domain. Third, we focus on scalable recommendation in retail and propose an efficient recommender system that explicitly models users' personal preferences that are reflected in their purchasing history

Incorporating health factors into food recommendation : experiments on real-world data from a weight-loss app

Les systèmes de recommandation typiques tentent d'imiter les comportements passés des utilisateurs pour faire des recommandations futures. Par exemple, dans le domaine des recommandations alimentaires, ces algorithmes de recommandation apprennent généralement d'abord l'historique de consommation de l'utilisateur, puis recommandent les aliments que l'utilisateur préfère. Bien qu'il existe de nombreux systèmes de recommandation d'aliments proposés dans la littérature, la plupart d'entre eux sont généralement des applications directes des algorithmes de recommandation génériques sur des ensembles de données alimentaires. Nous pensons que pour le problème de la recommandation alimentaire, les connaissances spécifiques au domaine joueraient un rôle vital dans la réussite d'un recommandeur alimentaire. Cependant, la plupart des modèles existants n'intègrent pas ces connaissances. Pour résoudre ce problème, dans cet article, nous intégrons des facteurs liés à la santé (tels que l'IMC des utilisateurs, les changements de poids sous-jacents, les calories des aliments candidats et les variétés d'aliments) dans des modèles de recommandations alimentaires séquentielles pour les utilisateurs qui souhaitent mieux gérer leur alimentation et poids. Les changements de poids sous-jacents des utilisateurs sont également traités comme leurs objectifs ou leurs intentions (perdre, maintenir ou prendre du poids). Le modèle proposé devrait adapter en douceur le flux d'articles recommandé vers l'objectif des utilisateurs en tenant compte des préférences de consommation et des facteurs de santé antérieurs de l'utilisateur. Pour étudier les meilleures stratégies pour incorporer des facteurs de santé spécifiques à un domaine dans les recommandations alimentaires, dans cette étude, nous proposons deux approches de modélisation: la recommandation du prochain article et la recommandation du prochain panier. Ces deux méthodes prennent la séquence passée d'aliments (noms d'aliments et calories) consommés par un utilisateur comme entrée et produisent une liste classée d'aliments pour le prochain aliment (Next-item) ou le lendemain (Next-basket). En outre, les recommandations de base sont améliorées sur la base des approches de pointe de chaque approche de modélisation, qui sont respectivement GRU4Rec~\cite{GRU4Rec} et LSTM hiérarchique. Pour étudier l'impact des facteurs de santé et ajuster le modèle vers un objectif, nous construisons des sous-modèles spécifiques pour chaque groupe d'utilisateurs en fonction de l'IMC et de l'intention. À savoir, les utilisateurs sont regroupés en obèses, en surpoids, normaux, sous-pondérés selon l'IMC. Leurs données (par semaines) sont segmentées en semaines de perte/gain/maintien de poids en fonction du changement de poids au cours de la semaine. Cette dernière segmentation vise à saisir les habitudes de consommation alimentaire liées au poids, qui est traité comme l'intention sous-jacente de l'utilisateur. Un modèle général formé sur l'ensemble des données historiques mixtes devrait capturer les habitudes générales de consommation alimentaire de tous les utilisateurs, tandis qu'un sous-modèle formé sur l'ensemble spécifique de données pour l'IMC et l'intention capture celles des groupes ou semaines correspondants. Pour un utilisateur au sein d'un groupe d'IMC et avec l'intention de changer de poids, nous appliquons le sous-modèle spécifique, combiné avec le modèle général, pour la recommandation alimentaire. Nos modèles sont formés sur une grande quantité de données de comportement alimentaire d'utilisateurs réels à partir d'une application de gestion du poids, où nous pouvons observer la consommation alimentaire quotidienne et le poids corporel de plusieurs utilisateurs. Lorsque nous combinons le modèle complet général avec les modèles spécifiques à l'IMC et spécifiques à l'intention avec un coefficient approprié, nous observons des améliorations significatives par rapport aux performances du modèle général basé à la fois sur la recommandation de l'article suivant et sur la recommandation du panier suivant. De plus, les sous-modèles spécifiques à l'IMC et spécifiques à l'intention se sont avérés utiles, ce qui donne de meilleurs résultats que le modèle complet général, tandis que les sous-modèles spécifiques à l'IMC ont plus d'impact que le modèle spécifique à l'intention. En pratique, pour un utilisateur qui a l'intention de perdre du poids, le système peut appliquer le modèle de résultat Perte de poids (avec l'IMC correspondant) à l'utilisateur. Cela tend à ajuster en douceur le modèle général de recommandation vers cet objectif. En outre, le niveau d'ajustement pourrait être contrôlé par le coefficient de combinaison de modèles. En d'autres termes, avec un coefficient plus élevé, le sous-modèle spécifique aura un impact plus important sur la prédiction du classement final des aliments, ce qui implique que le système donnera la priorité à la réalisation de l'objectif de l'utilisateur plutôt qu'à l'imitation de ses habitudes alimentaires précédentes. Cette stratégie est plus efficace que de toujours recommander certains types d'aliments hypocaloriques, qui ne sont pas appréciés par l'utilisateur. L'intention est alignée sur le résultat de poids réel au lieu de l'intention indiquée par l'utilisateur. Ce dernier s'avère beaucoup moins performant dans nos expérimentations.Typical recommender systems try to mimic the past behaviors of users to make future recommendations. For example, in the food recommendation domain, those recommenders typically first learn the user’s previous consumption history and then recommend the foods the user prefers. Although there are lots of food recommender systems proposed in the literature, most of them are usually some direct applications of generic recommendation algorithms on food datasets. We argue that for the food recommendation problem, domain-specific knowledge would play a vital role in a successful food recommender. However, most existing models fail to incorporate such knowledge. To address this issue, in this paper, we incorporate health-related factors (such as users’ BMI, underlying weight changes, calories of the candidate food items, and food varieties) in sequential food recommendation models for users who want to better manage their body weight. The users' underlying weight changes are also as treated as their goals or intents (either losing, maintaining, or gaining weight). The proposed model is expected to smoothly adapt the recommended item stream toward the users’ goal by considering the user’s previous consumption preferences and health factors. To investigate the best strategies to incorporate domain-specific health factors into food recommenders, in this study, we propose two modeling approaches: Next-item Recommendation and Next-basket Recommendation. These two methods take the past sequence of foods (food names and calories) consumed by a user as the input and produce a ranked list of foods for the next one (Next-item) or the next day (Next-basket). Besides, the basic recommendations are improved based on the state-of-the-art approaches of each modeling approach, which are GRU4Rec~\cite{GRU4Rec} and hierarchical LSTM, respectively. To investigate the impact of health factors and tune the model toward a goal, we build specific sub-models for each group of users according to BMI and intent. Namely, users are grouped into Obese, Overweighted, Normal, Underweighted according to BMI. Their data (by weeks) are segmented into weight losing/gaining/maintaining weeks according to the weight change during the week. This latter segmentation aims to capture food consumption patterns related to weight outcome, which is treated as the user's underlying intent. A general model trained on the whole mixed historical data is expected to capture the general food consumption patterns of all the users, while a sub-model trained on the specific set of data for BMI and intent captures those of the corresponding groups or weeks. For a user within a BMI group and with the intent of weight change, we apply the specific sub-model, combined with the general model, for food recommendation. Our models are trained on a large amount of eating behavior data of real users from a weight management app, where we can observe the daily food consumption and the body weight of many users. When we combine the general full-model with the BMI-specific and intent-specific models with appropriate coefficient, we observe significant improvements compared with the performance of the general model based on both Next-item Recommendation and Next-basket Recommendation. Furthermore, both BMI-specific and intent-specific sub-models have been proved useful, which achieves better results than the general full-model, while BMI-specific sub-models are more impactful than the intent-specific model. In practice, for a user who intends to lose weight, the system can apply the Losing-weight outcome model (with the corresponding BMI) to the user. This tends to smoothly adjust the general recommendation model toward this goal. Besides, the adjustment level could be controlled by the coefficient of model combination. In other words, with a larger coefficient, the specific sub-model will have a greater impact on predicting the final food ranking list, implying that the system will prioritize achieving the user's goal over mimicking their previous eating habits. This strategy is more effective than always recommending some types of low-calorie foods, which are not liked by the user. The intent is aligned with the actual weight outcome instead of the indicated intention by the user. This latter turns out to be much less successful in our experiments

Multicriteria Evaluation for Top-k and Sequence-based Recommender Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Timeout Reached, Session Ends?

Die Identifikation von Sessions zum Verständnis des Benutzerverhaltens ist ein Forschungsgebiet des Web Usage Mining. Definitionen und Konzepte werden seit über 20 Jahren diskutiert. Die Forschung zeigt, dass Session-Identifizierung kein willkürlicher Prozess sein sollte. Es gibt eine fragwürdige Tendenz zu vereinfachten mechanischen Sessions anstelle logischer Segmentierungen. Ziel der Dissertation ist es zu beweisen, wie unterschiedliche Session-Ansätze zu abweichenden Ergebnissen und Interpretationen führen. Die übergreifende Forschungsfrage lautet: Werden sich verschiedene Ansätze zur Session-Identifizierung auf Analyseergebnisse und Machine-Learning-Probleme auswirken? Ein methodischer Rahmen für die Durchführung, den Vergleich und die Evaluation von Sessions wird gegeben. Die Dissertation implementiert 135 Session-Ansätze in einem Jahr (2018) Daten einer deutschen Preisvergleichs-E-Commerce-Plattform. Die Umsetzung umfasst mechanische Konzepte, logische Konstrukte und die Kombination mehrerer Mechaniken. Es wird gezeigt, wie logische Sessions durch Embedding-Algorithmen aus Benutzersequenzen konstruiert werden: mit einem neuartigen Ansatz zur Identifizierung logischer Sessions, bei dem die thematische Nähe von Interaktionen anstelle von Suchanfragen allein verwendet wird. Alle Ansätze werden verglichen und quantitativ beschrieben sowie in drei Machine-Learning-Problemen (wie Recommendation) angewendet. Der Hauptbeitrag dieser Dissertation besteht darin, einen umfassenden Vergleich von Session-Identifikationsalgorithmen bereitzustellen. Die Arbeit bietet eine Methodik zum Implementieren, Analysieren und Evaluieren einer Auswahl von Mechaniken, die es ermöglichen, das Benutzerverhalten und die Auswirkungen von Session-Modellierung besser zu verstehen. Die Ergebnisse zeigen, dass unterschiedlich strukturierte Eingabedaten die Ergebnisse von Algorithmen oder Analysen drastisch verändern können.The identification of sessions as a means of understanding user behaviour is a common research area of web usage mining. Different definitions and concepts have been discussed for over 20 years: Research shows that session identification is not an arbitrary task. There is a tendency towards simplistic mechanical sessions instead of more complex logical segmentations, which is questionable. This dissertation aims to prove how the nature of differing session-identification approaches leads to diverging results and interpretations. The overarching research question asks: will different session-identification approaches impact analysis and machine learning tasks? A comprehensive methodological framework for implementing, comparing and evaluating sessions is given. The dissertation provides implementation guidelines for 135 session-identification approaches utilizing a complete year (2018) of traffic data from a German price-comparison e-commerce platform. The implementation includes mechanical concepts, logical constructs and the combination of multiple methods. It shows how logical sessions were constructed from user sequences by employing embedding algorithms on interaction logs; taking a novel approach to logical session identification by utilizing topical proximity of interactions instead of search queries alone. All approaches are compared and quantitatively described. The application in three machine-learning tasks (such as recommendation) is intended to show that using different sessions as input data has a marked impact on the outcome. The main contribution of this dissertation is to provide a comprehensive comparison of session-identification algorithms. The research provides a methodology to implement, analyse and compare a wide variety of mechanics, allowing to better understand user behaviour and the effects of session modelling. The main results show that differently structured input data may drastically change the results of algorithms or analysis

Privacy-Preserving Crowdsourcing-Based Recommender Systems for E-Commerce & Health Services

En l’actualitat, els sistemes de recomanació han esdevingut un mecanisme fonamental per proporcionar als usuaris informació útil i filtrada, amb l’objectiu d’optimitzar la presa de decisions, com per exemple, en el camp del comerç electrònic. La quantitat de dades existent a Internet és tan extensa que els usuaris necessiten sistemes automàtics per ajudar-los a distingir entre informació valuosa i soroll. No obstant, sistemes de recomanació com el Filtratge Col·laboratiu tenen diverses limitacions, com ara la manca de resposta i la privadesa. Una part important d'aquesta tesi es dedica al desenvolupament de metodologies per fer front a aquestes limitacions. A més de les aportacions anteriors, en aquesta tesi també ens centrem en el procés d'urbanització que s'està produint a tot el món i en la necessitat de crear ciutats més sostenibles i habitables. En aquest context, ens proposem solucions de salut intel·ligent (s-health) i metodologies eficients de caracterització de canals sense fils, per tal de proporcionar assistència sanitària sostenible en el context de les ciutats intel·ligents.En la actualidad, los sistemas de recomendación se han convertido en una herramienta indispensable para proporcionar a los usuarios información útil y filtrada, con el objetivo de optimizar la toma de decisiones en una gran variedad de contextos. La cantidad de datos existente en Internet es tan extensa que los usuarios necesitan sistemas automáticos para ayudarles a distinguir entre información valiosa y ruido. Sin embargo, sistemas de recomendación como el Filtrado Colaborativo tienen varias limitaciones, tales como la falta de respuesta y la privacidad. Una parte importante de esta tesis se dedica al desarrollo de metodologías para hacer frente a esas limitaciones. Además de las aportaciones anteriores, en esta tesis también nos centramos en el proceso de urbanización que está teniendo lugar en todo el mundo y en la necesidad de crear ciudades más sostenibles y habitables. En este contexto, proponemos soluciones de salud inteligente (s-health) y metodologías eficientes de caracterización de canales inalámbricos, con el fin de proporcionar asistencia sanitaria sostenible en el contexto de las ciudades inteligentes.Our society lives an age where the eagerness for information has resulted in problems such as infobesity, especially after the arrival of Web 2.0. In this context, automatic systems such as recommenders are increasing their relevance, since they help to distinguish noise from useful information. However, recommender systems such as Collaborative Filtering have several limitations such as non-response and privacy. An important part of this thesis is devoted to the development of methodologies to cope with these limitations. In addition to the previously stated research topics, in this dissertation we also focus in the worldwide process of urbanisation that is taking place and the need for more sustainable and liveable cities. In this context, we focus on smart health solutions and efficient wireless channel characterisation methodologies, in order to provide sustainable healthcare in the context of smart cities

Requirements engineering for explainable systems

Information systems are ubiquitous in modern life and are powered by evermore complex algorithms that are often difficult to understand. Moreover, since systems are part of almost every aspect of human life, the quality in interaction and communication between humans and machines has become increasingly important. Hence the importance of explainability as an essential element of human-machine communication; it has also become an important quality requirement for modern information systems. However, dealing with quality requirements has never been a trivial task. To develop quality systems, software professionals have to understand how to transform abstract quality goals into real-world information system solutions. Requirements engineering provides a structured approach that aids software professionals in better comprehending, evaluating, and operationalizing quality requirements. Explainability has recently regained prominence and been acknowledged and established as a quality requirement; however, there is currently no requirements engineering recommendations specifically focused on explainable systems. To fill this gap, this thesis investigated explainability as a quality requirement and how it relates to the information systems context, with an emphasis on requirements engineering. To this end, this thesis proposes two theories that delineate the role of explainability and establish guidelines for the requirements engineering process of explainable systems. These theories are modeled and shaped through five artifacts. These theories and artifacts should help software professionals 1) to communicate and achieve a shared understanding of the concept of explainability; 2) to comprehend how explainability affects system quality and what role it plays; 3) in translating abstract quality goals into design and evaluation strategies; and 4) to shape the software development process for the development of explainable systems. The theories and artifacts were built and evaluated through literature studies, workshops, interviews, and a case study. The findings show that the knowledge made available helps practitioners understand the idea of explainability better, facilitating the creation of explainable systems. These results suggest that the proposed theories and artifacts are plausible, practical, and serve as a strong starting point for further extensions and improvements in the search for high-quality explainable systems

Building bridges for better machines : from machine ethics to machine explainability and back

Be it nursing robots in Japan, self-driving buses in Germany or automated hiring systems in the USA, complex artificial computing systems have become an indispensable part of our everyday lives. Two major challenges arise from this development: machine ethics and machine explainability. Machine ethics deals with behavioral constraints on systems to ensure restricted, morally acceptable behavior; machine explainability affords the means to satisfactorily explain the actions and decisions of systems so that human users can understand these systems and, thus, be assured of their socially beneficial effects. Machine ethics and explainability prove to be particularly efficient only in symbiosis. In this context, this thesis will demonstrate how machine ethics requires machine explainability and how machine explainability includes machine ethics. We develop these two facets using examples from the scenarios above. Based on these examples, we argue for a specific view of machine ethics and suggest how it can be formalized in a theoretical framework. In terms of machine explainability, we will outline how our proposed framework, by using an argumentation-based approach for decision making, can provide a foundation for machine explanations. Beyond the framework, we will also clarify the notion of machine explainability as a research area, charting its diverse and often confusing literature. To this end, we will outline what, exactly, machine explainability research aims to accomplish. Finally, we will use all these considerations as a starting point for developing evaluation criteria for good explanations, such as comprehensibility, assessability, and fidelity. Evaluating our framework using these criteria shows that it is a promising approach and augurs to outperform many other explainability approaches that have been developed so far.DFG: CRC 248: Center for Perspicuous Computing; VolkswagenStiftung: Explainable Intelligent System