Vivian F. López Batista

XML Based Integration of Web, Mobile and Desktop Components in a Service Oriented Architecture

Component autonomy and easy composition are two of the main purposes of Service oriented Architectures. Recently, some multilayer frameworks supporting service abstraction and tier-integration facilities have been developed. They are especially useful for developing ubiquitous software systems where the presentation layers for different visualization devices are decoupled from the business logic layer, but services provided by this one can be easily accessed. In this work, we present the practical experience in the deployment of new frameworks such as JavaServer Faces, Spring and Hibernate in a multilayer architecture for an application endowed with three types of user interfaces: Web, for accessing with a classic browser, mobile Web, for accessing through different mobile devices, and a desktop interface for the administration functionality, supplied as remote services from the server.

A collaborative filtering method for music recommendation using playing coefficients for artists and users

Proposal of a collaborative filtering (CF) method for music recommendation.The method is based on user and artist characterization.Only playing information that can be implicitly obtained is needed.The proposal can be applied for both rating prediction and item recommendation.The method outperforms other CF approaches. The great quantity of music content available online has increased interest in music recommender systems. However, some important problems must be addressed in order to give reliable recommendations. Many approaches have been proposed to deal with cold-start and first-rater drawbacks; however, the problem of generating recommendations for gray-sheep users has been less studied. Most of the methods that address this problem are content-based, hence they require item information that is not always available. Another significant drawback is the difficulty in obtaining explicit feedback from users, necessary for inducing recommendation models, which causes the well-known sparsity problem. In this work, a recommendation method based on playing coefficients is proposed for addressing the above-mentioned shortcomings of recommender systems when little information is available. The results prove that this proposal outperforms other collaborative filtering methods, including those that make use of user attributes.

A System for Multi-label Classification of Learning Objects

The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of Learning Object (LO), which provides ubiquitous access to multiple and distributed educational resources in many repositories. This article presents a system that enables the recovery and classification of LO and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives. For this classification, it is used a special multi-label data mining designed for the LO ranking tasks. According to each position, the system is responsible for presenting the results to the end user. The learning process is supervised, using two major tasks in supervised learning from multi-label data: multi-label classification and label ranking.

Semantic Based Web Mining for Recommender Systems

Availability of efficient mechanisms for selective and personalized recovery of information is nowadays one of the main demands of Web users. In the last years some systems endowed with intelligent mechanisms for making personalized recommendations have been developed. However, these recommender systems present some important drawbacks that prevent from satisfying entirely their users. In this work, a methodology that combines an association rule mining method with the definition of a domain-specific ontology is proposed in order to overcome these problems in the context of a movies’ recommender system.

Recommendation of Songs in Music Streaming Services: Dealing with Sparsity and Gray Sheep Problems

The interest for providing users with suitable recommendations of songs and playlists has increased since online services for listening to music have become popular. Many methods for achieving this objective have been proposed, some of them addressed to solve well-known problems of recommender systems. However, music application domain has additional drawbacks such as the difficulty for obtaining content information and explicit ratings required by the most reliable recommender methods. In this work, a proposal for improving collaborative filtering methods is presented, whose main advantage is the use of data obtainable easily and automatically from music platforms. The method is based on a procedure for deriving ratings from user implicit behavior as well as on a new way of managing the gray-sheep problem without using content information.

Recommender System Based on Collaborative Filtering for Spotify’s Users

In recent years, with the rise of streaming services like Netflix or Spotify, recommender systems are becoming more and more necessary. The success of Spotify’s Discover Weekly, a music recommender system that suggests new songs to users every week, confirms the need to implement these recommender systems. In this paper we propose a methodology based on collaborative filtering to recommend music for Spotify’s users from an ordered list of the most played songs over a period of time.

Discovering Knowledge by Fuzzy Predicates in Compensatory Fuzzy Logic Using Metaheuristic Algorithms

Compensatory Fuzzy Logic (CFL) is a logical system that enables an optimal way of modeling knowledge. Its axiomatic character enables the work of natural language translation of logic, so it is used in knowledge discovery and decision-making.Obtaining LDC predicates with high values of truth is a general and flexible approach that can be used to discover patterns and new knowledge from data. This work proposes a method for knowledge discovery from obtaining LDC predicates, to obtain different structures of knowledge using a metaheuristic approach. A series of experiments and results descriptions of certains advantages for representing several patterns and tendencies from data is used to prove the proposed method.

Data integration in Cloud Computing environment

Information processed by applications is usually stored in databases or filing systems, allowing each system to use its own interface. As the files are not stored transparently, it is necessary to define data models to efficiently manage the information. This study proposes a process for storing information that follows the cloud paradigm defined in the +Cloud platform, which facilitates the transparent integration of different sources for the applications without requiring a description of relational database models.