Learning users’ assistance requirements with WATSON

Interface agents are computer programs that provide personalized assistance to users with their computerbased tasks. The interface agents developed so far have focused their attention on learning a user's preferences in a given application domain and on assisting him according to them. However, in order to personalize the interaction with users, interface agents should also learn how to best interact with each user and how to provide them assistance of the right sort at the right time. Particularly, an interface agent has to determine when the user wants a suggestion to solve a problem, when he requires only a warning about it, when he wants the agent to execute an action to deal with the problem and when he wants the agent to do just nothing. In this work we propose a learning algorithm, named WATSON, to tackle this problem. The WATSON algorithm enables an interface agent to adapt its behavior and its interaction with a user to the user's assistance requirements.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

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

Learning users’ assistance requirements with WATSON

Interface agents are computer programs that provide personalized assistance to users with their computerbased tasks. The interface agents developed so far have focused their attention on learning a user's preferences in a given application domain and on assisting him according to them. However, in order to personalize the interaction with users, interface agents should also learn how to best interact with each user and how to provide them assistance of the right sort at the right time. Particularly, an interface agent has to determine when the user wants a suggestion to solve a problem, when he requires only a warning about it, when he wants the agent to execute an action to deal with the problem and when he wants the agent to do just nothing. In this work we propose a learning algorithm, named WATSON, to tackle this problem. The WATSON algorithm enables an interface agent to adapt its behavior and its interaction with a user to the user's assistance requirements.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Electronic Journal of SADIO Special Issue on ASAI 2006

ASAI, the Argentine Symposium on Artificial Intelligence, is an annual event intended to be the main forum of the Artificial Intelligence (AI) community in Argentina. The symposium provides a forum for researchers and AI community members to discuss and exchange ideas and experiences on diverse topics of AI. The Eighth Argentine Symposium on Artificial Intelligence, ASAI 2006, was held during 4 – 5 September 2006, in Mendoza, Argentina. ASAI 2006 was part of the 35th JAIIO, the 35th Argentine Meetings on Informatics and Operations Research, organized by SADIO.Sociedad Argentina de Informática e Investigación Operativ

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

Solving Package Recommendation Problems with Item Relations and Variable Size

In this article, we explore an approach to solve the problem of recommending a package of items (each of them with a score and a cost ) to a user. In our approach, we consider two types of relations between items: dependency and incompatibility; and we also consider that the size of the package is not fixed but cost-driven. To this end, adaptations of existing package recommendation algorithms are proposed. We have evaluated the proposed approach in a specific domain and obtained promising results.Sociedad Argentina de Informática e Investigación Operativa (SADIO

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

An analysis on the impact of geolocation in recommending venues in location-based social networks

The pervasiveness of geo-located devices has opened new possibilities in recommender systems on social networks. In effect, Location-Based Social Networks or LBSNs are a relatively new breed of social networks that let users share their location by triggering ”check-in” events on venues, such as businesses or historical places. In this paper, we compare the performance of traditional rating and social-based similarity metrics against location-based metrics in a userbased collaborative filtering algorithm that recommends venues or places to visit. This analysis was performed on a large real-world dataset provided by the Yelp social network service. Our results show that, geo-located metrics perform as well as rating or social metrics for selecting like-minded users and, thus, to issue a recommendation.Sociedad Argentina de Informática e Investigación Operativ

Generación de consultas personalizadas en un LIM

En este trabajo se describe un agente capaz de observar al usuario mientras realiza consultas a un LlMS a través de Internet/Intranet. Este agente tiene la capacidad de deducir la rutina del usuario en relación al tipo de consultas realizadas. Se observa también, el momento en que el usuario realiza cada tipo de consulta. De esta manera, el agente construye el patrón de comportamiento para el usuario. El agente utiliza el conocimiento adquirido para inferir las consultas que realizaría un usuario en un determinado momento, siendo el objetivo final del agente realizar las consultas en forma autónoma un cierto tiempo antes de que sean requeridas. De este modo, los resultados están disponibles para el usuarío cuando él los solicita, sin las demoras ni costos adicionales que actualmente debe soportar. La información adquirida sobre las consultas relevantes para un usuario constituye el perfil de consultas del mismo. Este perfil es aumentado y actualizado utilizando el feedback que el usuario provee al sistema. El agente utiliza una técnica que combina la técnica de Razonamiento Basado en Casos y la técnica de Redes Bayesianas para la generación de consultas personalizadas. En la sección 2 se describe brevemente la integración de las técnicas de Razonamiento Basado en Casos y Redes de Bayes. En la sección 3 se describe el agente QueryMonitor que implementa esta técnica integrada. Finalmente, en la sección 4 se presentan las conclusiones.Eje: Sistemas inteligentes. Metaheurísticas.Red de Universidades con Carreras en Informática (RedUNCI

The IONWI algorithm: learning when and when not to interrupt

One of the key issues for an interface agent to succeed at assisting a user is learning when and when not to interrupt him to provide him assistance. Unwanted or irrelevant interruptions hinder the user’s work and make him dislike the agent because it is being intrusive and impolite. The IONWI algorithm enables interface agents to learn a user’s preferences and priorities regarding interruptions. The resulting user profile is then used by the agent to personalize the modality of the assistance, that is, assisting the user with an interruption or without an interruption depending on the user’s context. Experiments were conducted in the calendar management domain, obtaining promising results.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 1Red de Universidades con Carreras en Informática (RedUNCI