Músicos instrumentistas e cantores: margens e conteúdos para uma representação social

Entre Músicos, Instrumentistas e Cantores, uns revelarem-se ‘mais iguais’ que outros é conteúdo que prepassa dos ditos circulantes no grupo, assim contribuindo para a modelação da ‘adequada’ representação. Exposição da fase inicial de uma metodologia modelada no Quadro Teórico das Representações Sociais, este artigo aborda as representações dos três propondo a integração dos traços levantados no que é específico de cada subgrupo, e por fim, descodificando a diferenciação entre as representações à luz das tensões naturais dos relacionamentos intergrupais. Tratados qualitativamente, os principais dados da investigação consistem na opinião de 144 indivíduos recolhida pela utilização de questionários. Músicos das Áreas Teóricas, Instrumentistas, Cantores e Não Músicos, distribuídos por três estratos de envolvimento na actividade – estudantes do Ensino Complementar, Ensino Superior e Profissionais – revelam-nos que o pensado como intrínseco aos indivíduos, pode ser, de facto, fruto de uma elaboração socia

Offline Metrics for Evaluating Explanation Goals in Recommender Systems

Explanations are crucial for improving users' transparency, persuasiveness, engagement, and trust in Recommender Systems (RSs). However, evaluating the effectiveness of explanation algorithms regarding those goals remains challenging due to existing offline metrics' limitations. This paper introduces new metrics for the evaluation and validation of explanation algorithms based on the items and properties used to form the sentence of an explanation. Towards validating the metrics, the results of three state-of-the-art post-hoc explanation algorithms were evaluated for six RSs, comparing the offline metrics results with those of an online user study. The findings show the proposed offline metrics can effectively measure the performance of explanation algorithms and highlight a trade-off between the goals of transparency and trust, which are related to popular properties, and the goals of engagement and persuasiveness, which are associated with the diversification of properties displayed to users. Furthermore, the study contributes to the development of more robust evaluation methods for explanation algorithms in RSs

Leveraging hybrid recommenders with multifaceted implicit feedback

Research into recommender systems has focused on the importance of considering a variety of users’ inputs for an efficient capture of their main interests. However, most collaborative filtering efforts are related to latent factors and implicit feedback, which do not consider the metadata associated with both items and users. This article proposes a hybrid recommender model which exploits implicit feedback from users by considering not only the latent space of factors that describes the user and item, but also the available metadata associated with content and individuals. Such descriptions are an important source for the construction of a user’s profile that contains relevant and meaningful information about his/her preferences. The proposed model is generic enough to be used with many descriptions and types and characterizes users and items with distinguished features that are part of the whole recommendation process. The model was evaluated with the well-known MovieLens dataset and its composing modules were compared against other approaches reported in the literature. The results show its effectiveness in terms of prediction accuracy.FAPESPCNPqCAPE

Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization

In this paper, we propose a technique that uses multimodal interactions of users to generate a more accurate list of recommendations optimized for the user . Our approach is a response to the actual scenario on the Web which allows users to interact with the content in different ways, and thus, more information about his preferences can be obtained to improve recommendation. The proposal consists of an ensemble learning technique that combines rankings generated by unimodal recommenders based on particular interaction types. By using a combination of different types of feedback from users, we are able to provide better recommendations, as shown by our experimental evaluation

A sentiment-based item description approach for kNN collaborative filtering

In this paper, we propose an approach based on sentiment analysis to describe items in a neighborhood-based collaborative filtering model. We use unstructured users' reviews to produce a vector-based representation that considers the overall sentiment of those reviews towards specific features. We propose and compare two different techniques to obtain and score such features from textual content, namely term-based and aspect-based feature extraction. Finally, our proposal is compared against structured metadata under the same recommendation algorithm, whose results show a significant improvement over the baselines.FAPESP (process numbers 2013/10756-5, and 2013/22547-1

Optimizing personalized ranking in recommender systems with metadata awareness

In this paper, we propose an item recommendation algorithm based on latent factors which uses implicit feedback from users to optimize the ranking of items according to individual preferences. The novelty of the algorithm is the integration of content metadata to improve the quality of recommendations. Such descriptions are an important source to construct a personalized set of items which are meaningfully related to the user’s main interests. The method is evaluated on two diferente datasets, being compared against another approach reported in the literature. The results demonstrate the effectiveness of supporting personalized ranking with metadata awareness.CAPESCNPqFAPESP (grant #2013/22547-1 and #2012/13830-9

Combining multiple metadata types in movies recommendation using ensemble algorithms

In this paper, we analyze the application of ensemble algorithms to improve the ranking recommendation problem with multiple metadata. We propose three generic ensemble strategies that do not require modification of the recommender algorithm. They combine predictions from a recommender trained with distinct metadata into a unified rank of recommended items. The proposed strategies are Most Pleasure, Best of All and Genetic Algorithm Weighting. The evaluation using the HetRec 2011 MovieLens 2k dataset with five different metadata (genres, tags, directors, actors and countries) shows that our proposed ensemble algorithms achieve a considerable 7% improvement in the Mean Average\ud Precision even with state-of-art collaborative filtering algorithms

PERSONALIZED RANKING OF MOVIES: EVALUATING DIFFERENT METADATA TYPES AND RECOMMENDATION STRATEGIES

This paper proposes a study and comparison among a variety of metadata types in order to identify the most relevant pieces of information in order to identify the most relevant pieces of information in personalized ranking of movie items. We used four algorithms available in the literature to analyze the descriptions, and compared each other using the metadata extracted from two datasets, namely MovieLens and IMDB. As a result of our evaluation, we found out that the movies' genres and actors are the kind of description that generates better predictions for the considered content-based recommenders.This paper proposes a study and comparison among a variety of metadata types in order to identify the most relevant pieces of information in order to identify the most relevant pieces of information in personalized ranking of movie items. We used four algorithms available in the literature to analyze the descriptions, and compared each other using the metadata extracted from two datasets, namely MovieLens and IMDB. As a result of our evaluation, we found out that the movies' genres and actors are the kind of description that generates better predictions for the considered content-based recommenders

Improving personalized ranking in recommender systems with topic hierarchies and implicit feedback

The knowledge of semantic information about the content and user’s preferences is an important issue to improve recommender systems. However, the extraction of such meaningful metadata needs an intense and time-consuming human effort, which is impractical specially with large databases. In this paper, we mitigate this problem by proposing a recommendation model based on latent factors and implicit feedback which uses an unsupervised topic hierarchy constructor algorithm to organize and collect metadata at different granularities from unstructured textual content. We provide an empirical evaluation using a dataset of web pages written in Portuguese language, and the results show that personalized ranking with better quality can be generated using the extracted topics at medium granularity.CAPESCNPqFAPESP (grant #2013/25547-1 and #2012/13830-9

Applying multi-view based metadata in personalized ranking for recommender systems

In this paper, we propose a multi-view based metadata extraction technique from unstructured textual content in order to be applied in recommendation algorithms based on latent factors. The solution aims at reducing the problem of intense and time-consuming human effort to identify, collect and label descriptions about the items. Our proposal uses a unsupervised learning method to construct topic hierarchies with named entity recognition as privileged information. We evaluate the technique using different recommendation algorithms, and show that better accuracy is obtained when additional information about items is considered.São Paulo Research Foundation (FAPESP) (Grants 2012/13830-9, 2013/16039-3, 2013/22547-1)CAPE