In pursuit of satisfaction and the prevention of embarrassment : affective state in group recommender systems

Peer reviewedPostprin

APPLICATION OF MULTI-CRITERIA ANALYSIS BASED ON THE INDIVIDUAL PSYCHOLOGICAL PROFILE FOR RECOMMENDER SYSTEMS

This paper presents a novel approach for user classification exploiting multicriteriaanalysis. This method is based on measuring the distance between anobservation and its respective Pareto front. The obtained results show that thecombination of the standard KNN classification and the distance from Paretofronts gives satisfactory classification accuracy – higher than the accuracy obtainedfor each of these methods applied separately. Conclusions from thisstudy may be applied in recommender systems where the proposed methodcan be implemented as the part of the collaborative filtering algorithm

Enhancing Collaborative Filtering Using Implicit Relations in Data

International audienceThis work presents a Recommender System (RS) that relies on distributed recommendation techniques and implicit relations in data. In order to simplify the experience of users, recommender systems pre-select and filter information in which they may be interested in. Users express their interests in items by giving their opinion (explicit data) and navigating through the web-page (implicit data). The Matrix Fac-torization (MF) recommendation technique analyze this feedback, but it does not take more heterogeneous data into account. In order to improve recommendations, the description of items can be used to increase the relations among data. Our proposal extends MF techniques by adding implicit relations in an independent layer. Indeed, using past preferences, we deeply analyze the implicit interest of users in the attributes of items. By using this, we transform ratings and predictions into " semantic values " , where the term semantic indicates the expansion in the meaning of ratings. The experimentation phase uses MovieLens and IMDb database. We compare our work against a simple Matrix Factorization technique. Results show accurate personalized recommendations. At least but not at last, both recommendation analysis and semantic analysis can be par-allelized, alleviating time processing in large amount of data

Neural Network Based Multi-Criteria Ranking Prediction - Sustainability Assessment Case Study

Machine learning models are high-potential tools that enable prediction in problems incorpo- rating multiple attributes exploiting historical data. Prediction models are applicable in au- tonomous recommending systems development based on acquired datasets. They enable to profit from expert knowledge to support decision-makers in various fields. This paper demon- strates the application of an artificial neural network model named Multi-layer Perceptron (MLP) regressor for rankings prediction based on expert assessments performed in the past with multi- criteria decision analysis methods. The prediction given by the trained model shows high con- vergence with the real ranking. It proves that the MLP regressor has wide possibilities in de- veloping autonomous recommending systems that do not need the active participation of the decision-maker. The developed methodology was applied to predict European countries’ rank- ing regarding clean, affordable, and sustainable energy systems for the public in Sustainable Development Goal 7 (SDG 7)

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

A TIERED RECOMMENDER SYSTEM FOR COST-EFFECTIVE CLOUD INSTANCE SELECTION

Cloud computing has greatly impacted the scientific community and the end users. By leveraging cloud computing, small research institutions and undergraduate colleges are able to alleviate costs and achieve research goals without purchasing and maintaining all the hardware and software. In addition, cloud computing allows researchers to access resources as their teams require and allows real-time collaboration with team members across the globe. Nowadays however, users are easily overwhelmed by the wide range of cloud servers and instances. Due to differences between the cloud server platforms and between instances within the platform, users find it difficult to identify the right instance match for their application. Therefore, we propose the A2Cloud-Hierarchy (A2Cloud-H) framework that recommends Cloud instances to users for high-performance scientific computing. The framework comprises four components: training data collection, supervised learning (SL) module, unsupervised learning (USL) module, and a decision module. The training database comprise testing traces of previous application and Cloud instances; these are contributed by the scientific community. The SL module contains three popular supervised learning modules: logistic regression, support vector machine and random forest, which train using the database to qualitatively assess the instance performance for the target application. The USL module includes three collaborative filtering methods: application-based, instance-based and rank-based, which use the database to estimate the instances’ performance ratings for the target application. The decision module comprises multiple tiers of analytic hierarchy processing, which consolidate the instance recommendation from the SL and USL modules into a final instance recommendation. The model is trained and validated by 8 real-world applications on 20 Cloud instances, yielding more than 90% modeling accuracy. The recommendation and integration method proposed in this thesis can help promote a better cloud computing environment for both end-users and cloud server platforms

Multi-Layer Perceptron Regressor for Ranking Prediction in Information Systems for Sustainability Assessment

Machine learning models are powerful and valuable tools for predicting results for any problems represented by multiple variables based on information contained in historical data. Predictive models have significant development potential in autonomous decision support systems based on collected and processed data and expert knowledge to effectively support decision-makers. This paper presents the application of an artificial neural network model called Multi-layer Perceptron (MLP) regressor to predict rankings based on MCDA evaluations performed earlier with expert participation. The results predicted by the model trained on training data demonstrate high consistency with the real ranking, confirming the high potential of this model in building autonomous decision support systems. The proposed approach was applied to predict European countries’ ranking regarding environmentally friendly, efficient, and affordable energy

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

Assessing and improving recommender systems to deal with user cold-start problem

Recommender systems are in our everyday life. The recommendation methods have as main purpose to predict preferences for new items based on userŠs past preferences. The research related to this topic seeks among other things to discuss user cold-start problem, which is the challenge of recommending to users with few or no preferences records. One way to address cold-start issues is to infer the missing data relying on side information. Side information of different types has been explored in researches. Some studies use social information combined with usersŠ preferences, others user click behavior, location-based information, userŠs visual perception, contextual information, etc. The typical approach is to use side information to build one prediction model for each cold user. Due to the inherent complexity of this prediction process, for full cold-start user in particular, the performance of most recommender systems falls a great deal. We, rather, propose that cold users are best served by models already built in system. In this thesis we propose 4 approaches to deal with user cold-start problem using existing models available for analysis in the recommender systems. We cover the follow aspects: o Embedding social information into traditional recommender systems: We investigate the role of several social metrics on pairwise preference recommendations and provide the Ąrst steps towards a general framework to incorporate social information in traditional approaches. o Improving recommendation with visual perception similarities: We extract networks connecting users with similar visual perception and use them to come up with prediction models that maximize the information gained from cold users. o Analyzing the beneĄts of general framework to incorporate networked information into recommender systems: Representing different types of side information as a user network, we investigated how to incorporate networked information into recommender systems to understand the beneĄts of it in the context of cold user recommendation. o Analyzing the impact of prediction model selection for cold users: The last proposal consider that without side information the system will recommend to cold users based on the switch of models already built in system. We evaluated the proposed approaches in terms of prediction quality and ranking quality in real-world datasets under different recommendation domains. The experiments showed that our approaches achieve better results than the comparison methods.Tese (Doutorado)Sistemas de recomendação fazem parte do nosso dia-a-dia. Os métodos usados nesses sistemas tem como objetivo principal predizer as preferências por novos itens baseado no perĄl do usuário. As pesquisas relacionadas a esse tópico procuram entre outras coisas tratar o problema do cold-start do usuário, que é o desaĄo de recomendar itens para usuários que possuem poucos ou nenhum registro de preferências no sistema. Uma forma de tratar o cold-start do usuário é buscar inferir as preferências dos usuários a partir de informações adicionais. Dessa forma, informações adicionais de diferentes tipos podem ser exploradas nas pesquisas. Alguns estudos usam informação social combinada com preferências dos usuários, outros se baseiam nos clicks ao navegar por sites Web, informação de localização geográĄca, percepção visual, informação de contexto, etc. A abordagem típica desses sistemas é usar informação adicional para construir um modelo de predição para cada usuário. Além desse processo ser mais complexo, para usuários full cold-start (sem preferências identiĄcadas pelo sistema) em particular, a maioria dos sistemas de recomendação apresentam um baixo desempenho. O trabalho aqui apresentado, por outro lado, propõe que novos usuários receberão recomendações mais acuradas de modelos de predição que já existem no sistema. Nesta tese foram propostas 4 abordagens para lidar com o problema de cold-start do usuário usando modelos existentes nos sistemas de recomendação. As abordagens apresentadas trataram os seguintes aspectos: o Inclusão de informação social em sistemas de recomendação tradicional: foram investigados os papéis de várias métricas sociais em um sistema de recomendação de preferências pairwise fornecendo subsidíos para a deĄnição de um framework geral para incluir informação social em abordagens tradicionais. o Uso de similaridade por percepção visual: usando a similaridade por percepção visual foram inferidas redes, conectando usuários similares, para serem usadas na seleção de modelos de predição para novos usuários. o Análise dos benefícios de um framework geral para incluir informação de redes de usuários em sistemas de recomendação: representando diferentes tipos de informação adicional como uma rede de usuários, foi investigado como as redes de usuários podem ser incluídas nos sistemas de recomendação de maneira a beneĄciar a recomendação para usuários cold-start. o Análise do impacto da seleção de modelos de predição para usuários cold-start: a última abordagem proposta considerou que sem a informação adicional o sistema poderia recomendar para novos usuários fazendo a troca entre os modelos já existentes no sistema e procurando aprender qual seria o mais adequado para a recomendação. As abordagens propostas foram avaliadas em termos da qualidade da predição e da qualidade do ranking em banco de dados reais e de diferentes domínios. Os resultados obtidos demonstraram que as abordagens propostas atingiram melhores resultados que os métodos do estado da arte