Hybrid Recommender Systems: A Systematic Literature Review

Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models

MREPSA : modelo de recomendação de estratégias pedagógicas baseado em aspectos socioafetivos do aluno em ambiente virtual de aprendizagem

Esta pesquisa investiga como construir um modelo de recomendação que integra estratégias pedagógicas a partir dos aspectos socioafetivos do aluno em Ambiente Virtual de Aprendizagem (AVA). Considerando que os aspectos afetivos e sociais vivenciados pelo sujeito interferem sobre seu desenvolvimento cognitivo, é relevante que o docente acompanhe as alterações comportamentais de cada aluno em sala de aula e, especialmente, no AVA. Neste contexto, a mediação do professor através de estratégias pedagógicas (EP) personalizadas adequadas à situação socioafetiva do estudante é significativa e pode contribuir em prol do aprendizado. Estratégias pedagógicas são ações planejadas para atingir aos objetivos pretendidos na formação do alunado. O presente estudo foi realizado em uma abordagem qualitativa e quantitativa do tipo estudo de casos múltiplos. O público-alvo da investigação são professores de ensino superior que utilizaram o AVA ROODA - Rede cOOperativa De Aprendizagem como plataforma para o desenvolvimento de suas atividades de ensino. Neste escopo, a pesquisa foi desenvolvida em cinco etapas, a saber: 1) Realização de estudo teórico sobre as temáticas de Sistemas de Recomendação, Socioafetividade, segundo a epistemologia genética de Piaget, Traços de Personalidade e Estratégias Pedagógicas, visando ao embasamento teórico nas respectivas e a identificação de trabalhos correlatos. 2) Construção do Modelo de Recomendação de Estratégias Pedagógicas a partir do perfil socioafetivo do aluno em ambiente virtual de aprendizagem. 3) Implementação das definições do MREPSA em um sistema de recomendação no AVA de aplicação. 4) Avaliação do Modelo no AVA de aplicação. 5) Análise dos resultados. A coleta de dados foi realizada mediante a aplicação de questionário cujas respostas foram examinadas segundo a metodologia de Análise de Conteúdo. Nesta perspectiva, foram estabelecidas três categorias de análise: Categoria I – A efetividade do MREPSA como modelo de recomendação, Categoria II – A qualidade do MREPSA como modelo de recomendação e Categoria III – A pertinência das recomendações ofertadas a partir do MREPSA. Os resultados apresentados apontam que as recomendações fornecidas são pertinentes com os estados socioafetivos dos estudantes. Ressaltam, ainda, que as sugestões são adequadas e úteis como ferramenta de apoio ao professor. Desse modo, as EP auxiliam a compreender a situação em que o aluno se encontra, ao mesmo tempo que provêm sugestões de ação pedagógica em resposta ao momento que este está passando. Com isso, vislumbra-se que o modelo pode servir de base para o desenvolvimento de novas abordagens de recomendação baseadas em aspectos socioafetivos, as quais podem contribuir para que os docentes possam dar uma atenção mais personalizada às necessidades afetivas e sociais de seus alunos em Ambientes Virtuais de Aprendizagem, em especial, na Educação a Distância.This research investigates how to build a recommendation model that integrates pedagogical strategies based on the student's socio-affective aspects in the Virtual Learning Environment (VLE). Considering that the affective and social aspects experienced by the subject interfere with his cognitive development, it is relevant that the teacher accompanies the behavioral changes of each student in the classroom and, especially, in the VLE. In this context, the mediation of the teacher through personalized pedagogical strategies (EP) tailored to the student's socio-affective situation is significant and can contribute to learning. Pedagogical strategies are actions designed to achieve the intended educational objectives. The present study was carried out using a qualitative and quantitative approach, such as multiple case studies. The target audience of the investigation are higher education teachers who used the VLE ROODA as a platform for the development of their teaching activities. In this scope, the research was developed in five stages, namely: 1) Carrying out a theoretical study on the themes of Recommendation Systems, Socio-affectivity, according to Piaget's genetic epistemology, Personality Traits and Pedagogical Strategies, aiming at the theoretical foundation in the respective and the identification of related works. 2) Construction of the Pedagogical Strategies Recommendation Model based on the student's socio-affective profile in a virtual learning environment. 3) Implementation of MREPSA definitions in a recommendation system in the application AVA. 4) Evaluation of the Model in the VLE. 5) Analysis of the results. Data collection was performed by applying a questionnaire whose answers were examined according to the Content Analysis methodology. In this perspective, three categories of analysis were established: Category I - The effectiveness of MREPSA as a recommendation model, Category II - The quality of MREPSA as a recommendation model and Category III - The relevance of the recommendations offered from MREPSA. The results presented show that the recommendations provided are relevant to the students' socio-affective states. They also emphasize that the suggestions are adequate and useful as a tool to support the teacher. In this way, the EPs help to understand the situation in which the student is, while providing suggestions for pedagogical action in response to the moment he is going through. Thus, it is envisaged that the model can serve as a basis for the development of new recommendation approaches based on socio-affective aspects, which can contribute so that teachers can give a more personalized attention to the affective and social needs of their students in the Virtual Learning Environment, especially in Distance Education