Agent technology recommending personalized information and its evaluation

Shibata, Hirohito; Hoshiai, Takashige; Kubota, Minoru; Teramoto, M.

doi:10.1109/iwads.2002.1194668

Cited by 4 publications

(4 citation statements)

References 1 publication

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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%

A selective review of research in content personalization

Jeevan

Padhi²

2006

Library Review

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Purpose -To provide a selective bibliography in the emerging area of library content personalization for the benefit of library and information professionals. Design/methodology/approach -A range of recently published works (in the period 1993-2004), which aim to provide pragmatic application of content personalization rather than theoretical works, are discussed and sorted into ''classified'' sections to help library professionals understand more about the various options for formulating content as per the specific needs of their clientele. Findings -This paper provides information about each category of tool and technique of personalization, indicating what is achieved and how particular developments can help other libraries or professionals. It recognises that personalization of library resources is a viable way of helping users deal with the information explosion, conserving their time for more productive intellectual tasks. It identifies how computer and information technology has enabled document mapping to be more efficient, especially because of the ease with which a document can be indexed and represented with multiple terms, and confirms that this same functionality can be used to represent a user's interests, facilitating the easy linking of relevant sources to prospective users. Personalization of library resources is an effective way for maximizing user benefit. Research limitations/implications -This is not an exhaustive list of developments in personalization. Rather it identifies a mix of products and solutions that are of immediate use to librarians. Practical implications -A very useful source of pragmatic applications of personalization so far, that can guide a practicing professional interested in creating similar solutions for more productive information support in his/her library. Originality/value -This paper fulfils an identified need for a ''review of technology'' for LIS practitioners and offers practical help to any professional exploring solutions similar to those outlined in this paper.

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Section: Personalization Using Artificial Intelligencementioning

confidence: 99%

A selective review of research in content personalization

Jeevan

Padhi²

2006

Library Review

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“…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%

Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis

Guan

Chan

Zhu

2005

Electronic Commerce Research and Applications

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Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized ecommerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer's generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers' generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising.

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“…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

Arquitectura de búsqueda basada en técnicas soft computing para la resolución de problemas combinatorios en diferentes dominios de aplicación

Cubas¹

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Agent technology recommending personalized information and its evaluation

Cited by 4 publications

References 1 publication

A selective review of research in content personalization

A selective review of research in content personalization

Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis

Arquitectura de búsqueda basada en técnicas soft computing para la resolución de problemas combinatorios en diferentes dominios de aplicación

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