Redes Bayesianas para detección de roles de equipos en aprendizaje colaborativo soportado por computadoras

El trabajo colaborativo soportado por computadoras permite a los estudiantes que se encuentran en lugares remotos trabajar de manera conjunta en el mismo entorno virtual y permite la comunicación de ideas e información entre los integrantes del grupo. Sin embargo, como no todos los estudiantes son iguales, es importante estudiar las características de éstos para construir grupos de trabajo más productivos. La teoría de roles de equipo posibilita obtener buen desempeño en los equipos de trabajo considerando habilidades individuales, combinando las falencias de cada rol con las fortalezas de los otros. Generalmente, las personas tienen que completar extensos cuestionarios para poder determinar sus roles de equipo. En este trabajo, se propone un método alternativo para realizar esta detección a través de un sistema de aprendizaje colaborativo y a partir de la utilización de la técnica de Redes Bayesianas.Computer-supported collaborative learning allows students who are in different places to work together in the same virtual space, and supports the communication of ideas and information among learners. However, as not all students are identical, it is important to study users' characteristics to build more productive teams. Team Roles Theory allows obtaining very good team performance taking into account individual skills, combining the weaknesses of each role with the strengths of others. Originally, people have to complete extensive questionnaires to determine their team role. In this work we propose an alternative method to make this detection through a collaborative learning system and by using a Bayesian Network.Fil: Balmaceda, José María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

A study of neighbour selection strategies for POI recommendation in LBSNs

Location-based Recommender Systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of liked-minded people, so called neighbors, for prediction. Thus, an adequate selection of such neighbors becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbors in the context of a collaborative filtering based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighborhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from Location-based Social Networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbors based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area as well as to recommender systems developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.Fil: Rios, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Applying the technology acceptance model to evaluation of recommender systems

In general, the study of recommender systems emphasizes the efficiency of techniques to provide accurate recommendations rather than factors influencing users' acceptance of the system; however, accuracy alone cannot account for users' satisfying experience. Bearing in mind this gap in the research, we apply the technology acceptance model (TAM) to evaluate user acceptance of a recommender system in the movies domain. Within the basic TAM model, we incorporate a new latent variable representing self-assessed user skills to use a recommender system. The experiment included 116 users who answered a satisfaction survey after using a movie recommender system. The results evince that perceived usefulness of the system has more impact than perceived ease of use to motivate acceptance of recommendations. Additionally, users' previous skills strongly influence perceived ease of use, which directly impacts on perceived usefulness of the system. These findings can assist developers of recommender systems in their attempt to maximize users' experience.Fil: Armentano, Marcelo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Christensen, Ingrid Alina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentin

Using constraint satisfaction to aid group formation in CSCL

In computer-supported collaborative learning contexts, group formation is a key aspect, since different characteristics of students might influence the group performance. In this article, we present an intelligent assistant that models group formation as a weighted constraint satisfaction problem (WCSP). The assistant takes into account three students´ features, namely: psychological styles, team roles and social networks. The proposed WCSP approach is able to combine constraints and preferences both for individuals and groups. The main goal of the assistant is to aid teachers to form learning groups considering factors such as team role balance and distribution of psychological styles. We also describe a pilot study to evaluate our proposal in different scenarios.Fil: Diaz Pace, Jorge Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Balmaceda, José María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentin

Group recommender systems: A multi-agent solution

Providing recommendations to groups of users has become a promising research area, since many items tend to be consumed by groups of people. Various techniques have been developed aiming at making recommendations to a group as a whole. Most works use aggregation techniques to combine preferences, recommendations or profiles. However, satisfying all group members in an even way still remains as a challenge. To deal with this problem, we propose an extension of a multi-agent approach based on negotiation techniques for group recommendation. In the approach, we use the multilateral Monotonic Concession Protocol (MCP) to combine individual recommendations into a group recommendation. In this work, we extend the MCP protocol to allow users to personalize the behavior of the agents. This extension was evaluated in two different domains (movies and points of interest) with satisfactory results. We compared our approach against different baselines, namely: a preference aggregation algorithm, a recommendation aggregation algorithm, and a simple one-step negotiation. The results show evidence that, when using our negotiation approach, users in the groups are more uniformly satisfied than with traditional aggregation approaches.Fil: Villavicencio, Christian Paulo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Diaz Pace, Jorge Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Personalized architectural documentation based on stakeholders' information needs

The stakeholders of a software system are, to a greater or lesser extent, concerned about its software architecture, as an essential artifact for capturing the key design decisions of the system. The architecture is normally documented in the Software Architecture Document (SAD), which tends to be a large and complex technical description, and does not always address the information needs of every stakeholder. Individual stakeholders are interested in dierent, sometimes overlapping, subsets of the SAD and they also require varying levels of detail. As a consequence, stakeholders are aected by an information overload problem, which in practice discourages the usage of the architectural knowledge and diminishes its value for the organization. Along this line, this work presents a semi-automated approach to recommend relevant contents of a given SAD to specific stakeholder profiles. Our approach assumes that SADs are hosted in Wikis, which not only favor communication and interactions among stakeholders, but also enable us to apply User Profiling techniques to infer stakeholders´ interests with respect to particular documents. We have built a recommendation tool implementing our approach, which was tested in two experiments with Wiki-based SADs. Although preliminary, the results have shown that the recommendations of the tool help to nd the architectural documents that best match the stakeholders´ interests.Fil: Tommasel, Antonela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Nicoletti, Matías Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Diaz Pace, Jorge Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentin

DPM: A novel distributed large-scale social graph processing framework for link prediction algorithms

Large-scale graphs have become ubiquitous in social media. Computer-based recommendations in these huge graphs pose challenges in terms of algorithm design and resource usage efficiency when processing recommendations in distributed computing environments. Moreover, recommendation algorithms for graphs, particularly link prediction algorithms, have different requirements depending of the way the underlying graph is traversed. Path-based algorithms usually perform traversals in different directions to build a large ranking of vertices to recommend, whereas random walk-based algorithms build an initial subgraph and perform several iterations on those vertices to compute the final ranking. In this work, we propose a distributed graph processing framework called Distributed Partitioned Merge (DPM), which supports both types of algorithms and we compare its performance and resource usage w.r.t. two relevant frameworks, namely Fork-Join and Pregel. In our experiments, we show that in most tests DPM outperforms both Pregel and Fork-Join in terms of recommendation time, with a minor penalization in network usage in some scenarios.Fil: Corbellini, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Using negotiation for group recommendation: a user-study on the movies domain

Providing recommendations to groups of users has become popular in many applications today. Even though there are several group recommendation techniques, the generation of recommendations that satisfy the group members in an even way remains a challenge. Because of this, we have developed a multi-agent approach called MAGReS that relies on negotiation techniques to improve group recommendations. Our approach was tested (on the movies domain) using synthetic data with satisfactory results. Given that the results when using synthetic data may sometimes differ with reality, we decided to assess MAGReS using data from real users. The results obtained showed firstly that, in comparison with the recommendations produced by a traditional approach, the recommendations of MAGReS produce a greater level of satisfaction to the group, and secondly that the proposed approach was able to predict more accurately the satisfaction levels of the group members. Finally, we could obtain some preliminary feedback regarding the explanations provided by the recommender system.Fil: Villavicencio, Christian Paulo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Diaz Pace, Jorge Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Un nuevo enfoque para el descubrimiento de conocimiento en el contexto de usabilidad Web

La usabilidad juega un papel clave en el aumento de la satisfacción del usuario y proporciona numerosos beneficios a los sitios web. Los sitios exitosos y fáciles de usar son el resultado de un proceso de diseño centrado en el usuario con un fuerte énfasis en la evaluación de la usabilidad, una tarea que puede ser larga y costosa. Para afrontar esta situación, se presenta el desarrollo de un proceso de evaluación con enfoque heurístico basado en los componentes de usabilidad definidos por J. Nielsen (Aprendizaje, Eficiencia, Memorización, Prevención de Errores y Satisfacción), al que se aplican técnicas de descubrimiento de conocimiento para determinar las relaciones entre diferentes heurísticas y componentes, con el fin de encontrar patrones en problemas de usabilidad. Se realizaron cinco estudios para encontrar diferentes tipos de relaciones utilizando técnicas de minería de datos, obteniendo varios patrones que los diseñadores o desarrolladores podrían utilizar para priorizar sus esfuerzos en la mejora de los sitios web, ya sea en los diseños de interfaz de usuario o en las características que ofrecen.Usability plays a key role in increasing user satisfaction and provides numerous benefits to websites. Successful and easy-to-use sites are the result of a user-centered design process with a strong emphasis on usability assessment, a task that can be lengthy and costly. To deal with this situation, we present the development of an evaluation process with a heuristic approach based on the usability components defined by J. Nielsen (Learning, Efficiency, Memorization, Error Prevention and Satisfaction), to which we apply knowledge discovery techniques to determine the relationships between different heuristics and components, in order to discover patterns in usability problems. Five studies were performed to find different types of relationships using data mining techniques, obtaining several patterns that designers or developers could use to prioritize their efforts in improving websites, either in user interface designs or in features they offer.Fil: Chavez Boza, Betty Margarita. Universidad Técnica Estatal de Quevedo; EcuadorFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Teyseyre, Alfredo Raul. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Portilla, Elías. Universidad Técnica Estatal de Quevedo; EcuadorFil: Erazo, Orlando. Universidad Técnica Estatal de Quevedo; Ecuado

Social Influence in Group Recommender Systems

Purpose – The purpose of this paper is to propose an approach to generate recommendations for groups on the basis of social factors extracted from a social network. Group recommendation techniques traditionally assumed users were independent individuals, ignoring the effects of social interaction and relationships among users. In this work the authors analyse the social factors available in social networks in the light of sociological theories which endorse individuals’ susceptibility to influence within a group. Design/methodology/approach – The approach proposed is based on the creation of a group model in two stages: identifying the items that are representative of the majority's preferences, and analysing members’ similarity; and extracting potential influence from members’ interactions in a social network to predict a group's opinion on each item. Findings – The promising results obtained when evaluating the approach in the movie domain suggest that individual opinions tend to be accommodated to group satisfaction, as demonstrated by the incidence of the aforementioned factors in collective behaviour, as endorsed by sociological research. Moreover the findings suggest that these factors have dissimilar impacts on group satisfaction. Originality/value – The results obtained provide clues about how social influence exerted within groups could alter individuals’ opinions when a group has a common goal. There is limited research in this area exploring social influence in group recommendations; thus the originality of this perspective lies in the use of sociological theory to explain social influence in groups of users, and the flexibility of the approach to be applied in any domain. The findings could be helpful for group recommender systems developers both at research and commercial levels.Fil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Christensen, Ingrid Alina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentin