Edward Rolando Núñez-Valdéz

Implicit feedback techniques on recommender systems applied to electronic books

The goal of this research is to define and capture a series of parameters that allowed us to perform a comparative analysis and find correlations between explicit and implicit feedback on recommender systems. Most of these systems require explicit actions from the users, such as rating, and commenting. In the context of electronic books this interaction may alter the patterns of reading and understanding of the users, as they are asked to stop reading and rate the content. By simulating the behavior of an electronic book reader we have improved the feedback process, by implicitly capturing, measuring, and classifying the information needed to discover user interests. In these times of information overload, we can now develop recommender systems that are mostly based on the users behavior, by relying on the obtained results.

Gade4all: Developing Multi-platform Videogames based on Domain Specific Languages and Model Driven Engineering

The development of applications for mobile devices is a constantly growing market which and more and more enterprises support the development of applications for this kind of devices. In that sense, videogames for mobile devices have become very popular worldwide and are now part of highly profitable and competitive industry. Due to the diversity of platforms and mobile devices and the complexity of this kind of applications, the development time and the number of errors within that development process have increased. The productivity of the developers has also decreased due to the necessity of using many programming languages in the development process. One of the most popular strategies is to employ specialized people to perform the development tasks more efficiently, but this involves an increase of the costs, which makes some applications economically unviable. In this article we present the Gade4all Project, consisting in a new platform that aims to facilitate the development of videogames and entertainment software through the use of Domain Specific Languages and Model Driven Engineering. This tool makes possible for users without previous knowledge in the field of software development to create 2D videogames for multiplatform mobile devices in a simple and innovative way.

A model-driven approach to generate and deploy videogames on multiple platforms

Currently, videogame development for mobile devices is a highly profitable and competitive industry worldwide. This profitability can be ascribed to the popularity that new technologies such as smartphones and tablets have acquired over the last few years. To increase competitiveness, we use a model driven engineering (MDE) approach to develop multi-platform videogames in a quick and easy manner. MDE helps us to reduce the number of errors, the amount of time required and the development costs and favors a productivity increment. The aim of this work is to present an approach for the agile development of multi-platform videogames by using high-level abstraction models. We offer a feasible solution supported by a tool that allows the development of videogames in a simple and agile way. An empirical validation is presented through a use case where the viability of our approach is shown in comparison with that of other tools. Finally, a survey shows the users’ perception of the solution with the objective of validating the use case results. The given use case indicates that our approach is suitable for improving videogame development. The survey indicates that users think that it is possible to model videogames and generate the corresponding applications with our approach.

Social Recommender System: A Recommender System based on Tweets for points of interest

Social networks are an inexhaustible source of knowledge. One of its main uses is to express opinions about a particular product or service. Thus, users usually write opinions about places or so-called points of interest. That is a valuable knowledge for other users and especially for users who know and trust the opinions of the writers. In this work, we present an overview of a platform to capture opinions related to places and generate a positive or a negative recommendation for users based on both the global use of Twitter as well as the comments made by trusted people on the network.

A recommender system based on implicit feedback for selective dissemination of ebooks

Abstract In this study, we describe a recommendation system for electronic books. The approach is based on implicit feedback derived from user’s interaction with electronic content. User’s behavior is tracked through several indicators that are subsequently used to feed the recommendation engine. This component then provides an explicit rating for the material interacted with. The role of this engine could be modeled as a regression task where content is rated according to the mentioned indicators. In this context, we benchmark twelve popular machine learning algorithms to perform this final function and evaluate the quality of the output provided by the system.

An Application for Recommender Systems in the Contents Industry

This paper presents and describes the construction of a recommender system model that classifies input information to yield product recommendations. This model was applied within the contents industry [1] by using artificial neural networks (particularly adaptive resonance theory) as an intelligent agent. The Netflix Prize data base was used and the model’s validation and simulation was written using Matlab®.