The 2nd International Workshop on Autonomous Decentralized System, 2002.
DOI: 10.1109/iwads.2002.1194668
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Agent technology recommending personalized information and its evaluation

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Cited by 4 publications
(4 citation statements)
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“…Liu et al (2001) propose an integrated framework using XML-based metadata models, user models and agents to develop personalized ecatalogs with a multi-level architecture to facilitate resource discovery and format translation, using three-level user profiles to model users' shopping interests into resources, categories and goods. Shibata et al (2002) propose a method of implementing active autonomous agents that discover and recommend contents matching with user preferences by observing user reactions to agent recommended contents without user guidance. The University of Michigan Digital Library Project, USA employs an architecture based on distributed information agents (Wellman et al, 1996).…”
Section: Personalization Using Artificial Intelligencementioning
confidence: 99%
“…Liu et al (2001) propose an integrated framework using XML-based metadata models, user models and agents to develop personalized ecatalogs with a multi-level architecture to facilitate resource discovery and format translation, using three-level user profiles to model users' shopping interests into resources, categories and goods. Shibata et al (2002) propose a method of implementing active autonomous agents that discover and recommend contents matching with user preferences by observing user reactions to agent recommended contents without user guidance. The University of Michigan Digital Library Project, USA employs an architecture based on distributed information agents (Wellman et al, 1996).…”
Section: Personalization Using Artificial Intelligencementioning
confidence: 99%
“…The agents retrieve the required information about their customer's preference structures. In another research, Shibata et al [11] proposed an approach in which autonomous agents can learn customerpreference by observing the customer's reaction to contents recommended by agents.…”
Section: Introductionmentioning
confidence: 99%
“…Guan et al [10] captured customer preference by requesting the customer to select the best product from a short list of products before adjusting the weights according to the feedback. A similar approach [11] also assigned weights to attributes, and these attributes weights were adjusted through reinforcement learning. Though weights were being used to improve the robustness of the system, they are not flexible enough for true optimization.…”
Section: Introductionmentioning
confidence: 99%
“…A partir de sus respuestas, el sistema va ajustando los pesos asignados a los diferentes atributos de losítems estudiados. Otro ejemplo de aplicación es el presentado por [Shibata et al, 2002], donde agentes de recomendación de información personalizada seleccionan y recomiendan contenidos a sus usuarios.…”
Section: Resultsunclassified