Learning Binary Preference Relations : Analysis of Logic-based versus Statistical Approaches

It is a truth universally acknowledged that e-commerce platform users in search of an item that best suits their preferences may be offered a lot of choices. An item may be characterised by many attributes, which can complicate the process. Here the classic approach in decision support systems – to put weights on the importance of each attribute – is not always helpful as users may find it hard to formulate their priorities explicitly. Pairwise comparisons provide an easy way to elicitate the user’s preferences in the form of the simplest possible qualitative preferences, which can then be combined to rank the available alternatives. We focus on this type of preference elicitation and learn the individual preference by applying one statistical approach based on Support Vector Machines (SVM), and two logic-based approaches: Inductive Logic Programming (ILP) and Decision Trees. All approaches are compared on a dataset of car preferences collected from human participants. While in general, the statistical approach has proven its practical advantages, our experiment shows that the logic-based approaches offer a number of benefits over the one based on statistics

A comparison of services for intent and entity recognition for conversational recommender systems

Conversational Recommender Systems (CoRSs) are becoming increasingly popular. However, designing and developing a CoRS is a challenging task since it requires multi-disciplinary skills. Even though several third-party services are available for supporting the creation of a CoRS, a comparative study of these platforms for the specific recommendation task is not available yet. In this work, we focus our attention on two crucial steps of the Conversational Recommendation (CoR) process, namely Intent and Entity Recognition. We compared four of the most popular services, both commercial and open source. Furthermore, we proposed two custom-made solutions for Entity Recognition, whose aim is to overcome the limitations of the other services. Results are very interesting and give a clear picture of the strengths and weaknesses of each solution

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

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

Pairwise Preferences Learning for Recommender Systems

Preference learning (PL) plays an important role in machine learning research and practice. PL works with an ordinal dataset, used frequently in areas such as behavioural science, medical science, education, psychology and social science. The aim of PL is to predict the preference for a new set of items based on the training data. In the application area of Recommender Systems (RSs), PL is used as an important element to produce good recommendations. Many ideas have been developed to build better recommendation techniques. One of the challenges in RSs is how to develop systems that are proactive and unobtrusive. To address this problem, we have studied the use of pairwise comparisons in preference elicitation as a very simple way of expressing preferences. Research in PL has also discovered this kind of representation and considers it to be learning from binary relations. There are three contributions in this thesis: The first and the most significant contribution is a new approach based on Inductive Logic Programming (ILP) in Description Logics (DL) representation to learn the relation of order. The second contribution is a strategy based on Active Learning (AL) to support the inference process and make choices more informative for learning purposes. A third contribution is a recommender system algorithm based on the ILP in DL approach, implemented in a real-world recommender system with a large used-car dataset. The proposed approach has been evaluated by using both offline and online experiments. The offline experiments were performed using two publicly available preference datasets, while the online experiment was conducted using 24 participants to evaluate the system. In the offline experiments, the overall accuracy of our proposed approach outperformed the other 3 baseline algorithms, SVM, Decision Tree and Aleph. In the online experiment, the user study also showed some satisfactory results in which our proposed pairwise comparisons interface in a recommender system beat a common standard list interface