Learn sesame a learning agent engine

Çağlayan, Alper; Snorrason, Magnus; Jacoby, J.Z.; Mazzu, J. M.; Jones, Robin; Kumar, Krishna

doi:10.1080/088395197118109

Cited by 25 publications

(16 citation statements)

References 4 publications

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“…This initial set of ratings can be obtained either explicitly from the user or using various implicit rating elicitation methods [Konstan et al 1997;Caglayan et al 1997;Oard & Kim 1998]. Moreover, the initial set of ratings does not have to be specified at the bottom level of the hierarchy.…”

Section: R(john Doe Action) := Aggr Xgenre=action R(john Doe X)mentioning

confidence: 99%

“…For example, rating R(101,7,1) = 6 in Figure 1 means that for the user with User ID 101 and the item with Item ID 7, rating 6 was specified during the weekday. The rating function R in (9) is usually defined as a partial function, where the initial set of ratings is either explicitly specified by the user or is inferred from the application [Konstan et al 1997;Caglayan et al 1997;Oard & Kim 1998]. Then one of the central problems in recommender systems is to estimate the unknown ratings, i.e., make the rating function R total.…”

Section: Multiple Dimensionsmentioning

confidence: 99%

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Incorporating contextual information in recommender systems using a multidimensional approach

Adomavicius

Sankaranarayanan

Sen

et al. 2005

ACM Trans. Inf. Syst.

980

672

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The paper presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, extensive profiling, and hierarchical aggregation of recommendations. The paper also presents a multidimensional rating estimation method capable of selecting two-dimensional segments of ratings pertinent to the recommendation context and applying standard collaborative filtering or other traditional two-dimensional rating estimation techniques to these segments. A comparison of the multidimensional and two-dimensional rating estimation approaches is made, and the tradeoffs between the two are studied. Moreover, the paper introduces a combined rating estimation method that identifies the situations where the MD approach outperforms the standard two-dimensional approach and uses the MD approach in those situations and the standard two-dimensional approach elsewhere. Finally, the paper presents a pilot empirical study of the combined approach, using a multidimensional movie recommender system that was developed for implementing this approach and testing its performance.2

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Section: R(john Doe Action) := Aggr Xgenre=action R(john Doe X)mentioning

confidence: 99%

Section: Multiple Dimensionsmentioning

confidence: 99%

Incorporating contextual information in recommender systems using a multidimensional approach

Adomavicius

Sankaranarayanan

Sen

et al. 2005

ACM Trans. Inf. Syst.

980

672

View full text Add to dashboard Cite

show abstract

“…Examples of foremost user modeling servers are: DOPPELÄNGER (Orwant 1995); Learn Sesame (Caglayan et al 1997); GroupLens (Konstan et al 1997); LMS (Machado et al 1999); PersonisAD (Assad et al 2007); MEDEA (Trella et al 2003); Cumulate (Brusilovsky 2004); UMS (Kobsa and Fink 2006). 5 Despite the evident benefits of centralized user modeling systems, they show some potential weaknesses (Kobsa 2007b).…”

Section: Centralized Approachesmentioning

confidence: 97%

User model interoperability: a survey

Carmagnola

Cena

Gena

2011

User Model User-Adap Inter

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Nowadays a large number of user-adaptive systems has been developed. Commonly, the effort to build user models is repeated across applications and domains, due to the lack of interoperability and synchronization among user-adaptive systems. There is a strong need for the next generation of user models to be interoperable, i.e. to be able to exchange user model portions and to use the information that has been exchanged to enrich the user experience. This paper presents an overview of the wellestablished literature dealing with user model interoperability, discussing the most representative work which has provided valuable solutions to face interoperability issues. Based on a detailed decomposition and a deep analysis of the selected work, we have isolated a set of dimensions characterizing the user model interoperability process along which the work has been classified. Starting from this analysis, the paper presents some open issues and possible future deployments in the area.

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“…OpenSesame! [30] Agentes y predictivo Asistente para Macintosh 7 que ofrece la realización de tareas comunes como abrir o cerrar archivos o aplicaciones, vaciar la papelera de reciclaje, etc. Reactive Keyboard [18] predictivo Asistente que predice el texto que el usuario escribirá tras comenzar a escribir las palabras.…”

Section: Pbdunclassified

Algoritmo Novedoso Para la Detección de Tareas Repetitivas en el Teclado

González¹,

Sánchez²

2015

invinning

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In this paper a tool for the detection of repetitive tasks with logical sequences realized across command of the keyboard is proposed, by means of the design and implementation of an algorithm based on the use of finite automata deterministic and agents of bosses' search. The innovation of the developed algorithm takes root in that it is orientated to the detection of repetitive tasks which activities have a logical sequence and that nowadays are not automated by the complex thing that is this labor. Palabras clave:Automatización de tareas repetitivas, algoritmos para la detección de patrones, comandos de teclado, autómatas. ResumenEn este artículo se propone una herramienta para la detección de tareas repetitivas con secuencias lógicas realizadas a través de comandos del teclado, mediante el diseño e implementación de un algoritmo basado en el uso de autómatas finitos determinísticos y agentes de búsqueda de patrones. La novedad del algoritmo desarrollado radica en que está orientado a la detección de tareas repetitivas cuyas actividades tienen una secuencia lógica y que actualmente no se encuentran automatizadas por lo complejo que es esta labor.

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Learn sesame a learning agent engine

Cited by 25 publications

References 4 publications

Incorporating contextual information in recommender systems using a multidimensional approach

Incorporating contextual information in recommender systems using a multidimensional approach

User model interoperability: a survey

Algoritmo Novedoso Para la Detección de Tareas Repetitivas en el Teclado

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