A Negotiation-Style Recommender Based on Computational Ecology in Open Negotiation Environments

Rosa, Josep Lluís de la; Hormazábal, Nicolás; Aciar, Silvana; Lopardo, Gabriel Alejandro; Trias, Albert; Montaner, Miquel

doi:10.1109/tie.2009.2027917

Cited by 26 publications

(7 citation statements)

References 17 publications

(16 reference statements)

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“…To help business users select trusted online auction sellers, a recommender system was designed [83] in which trading relationships are used to calculate the level of recommendations. Recommender systems were also applied in digital ecosystems where agents negotiate services on behalf of a number of small companies [84]. To build stable digital business ecosystems by means of improved collective intelligence, a model of negotiation-style dynamics from the point of view of computational ecology was introduced in [84], which inspires an ecosystem monitor and a novel negotiation-style recommender.…”

Section: E-business Recommender Systemsmentioning

confidence: 99%

“…Recommender systems were also applied in digital ecosystems where agents negotiate services on behalf of a number of small companies [84]. To build stable digital business ecosystems by means of improved collective intelligence, a model of negotiation-style dynamics from the point of view of computational ecology was introduced in [84], which inspires an ecosystem monitor and a novel negotiation-style recommender. To help private bankers provide suitable investment portfolios to their clients, a multi-investment recommender system PB-ADVISOR was presented [85].…”

Section: E-business Recommender Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Recommender system application developments: A survey

Mao

et al. 2015

Decision Support Systems

1,306

546

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A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobilebased platforms). Some significant new topics are identified and listed as new directions. By providing a stateof-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications.

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Section: E-business Recommender Systemsmentioning

confidence: 99%

Section: E-business Recommender Systemsmentioning

confidence: 99%

Recommender system application developments: A survey

Mao

et al. 2015

Decision Support Systems

1,306

546

View full text Add to dashboard Cite

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“…Studies in the DBE technologies sub-theme examine how technical innovations such as recommender systems, autonomic monitoring, collective intelligence and data mining techniques support DBEs' developments and operations (De La Rosa et al, 2011). In particular, these studies stress the need for standardization of DBE technologies to foster seamless interoperability between systems (Korpela et al, 2017).…”

Section: Technical Issues Themementioning

confidence: 99%

Digital business ecosystem: Literature review and a framework for future research

Senyo

Liu

Effah

2019

International Journal of Information Management

300

174

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Digital innovation has radically changed how organisations collaborate and compete. Coupled with this change are new collaborative value creation networks such as digital business ecosystems (DBEs). DBE is a socio-technical network of individuals, organisations and technologies that collectively co-create value. Since the emergence of DBE over a decade ago, there have been limited attempts to critically review and synthesise the body of knowledge presented over the years. Thus, the purpose of this paper is to fill this gap in DBE research by: (1) developing a comprehensive framework that synthesises and provides an overall direction of DBE research; (2) pointing out gaps in DBE literature; and (3) providing future research directions. To address this purpose, we systematically analysed 101 research articles on DBE. The findings provide insightful revelations to address some limitations in the current DBE research. As such, this study makes important contributions and serves as a useful resource for future DBE studies and practice.

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“…Synergy works on computational ecologies [25] show how this approach might work: the SPDOs ask themselves how much preservation is necessary (appraise) and, according to a DP budget that would be regularly assigned to them, compete with each other for the preservation services.…”

Section: Background and Related Workmentioning

confidence: 99%

Analysis of Nature-Inspired Algorithms for Long-Term Digital Preservation

El-Fakdi

Rosa

2021

Mathematics

Self Cite

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Digital preservation is a research area devoted to keeping digital assets preserved and usable for many years. Out of the many approaches to digital preservation, the present research article follows a new object-centered digital preservation paradigm where digital objects share part of the responsibility for preservation: they can move, replicate, and evolve to a higher-quality format inside a digital ecosystem. In the new framework, the behavior of digital objects needs to be modeled in order to obtain the best preservation strategy. Thus, digital objects are programmed with the mission of their own long-term self-preservation, which entails being accessible and reproducible by users at any time in the future regardless of frequent technological changes due to software and hardware upgrades. Three nature-inspired computational intelligence algorithms, based on the collective behavior of decentralized and self-organized systems, were selected for the modeling approach: multipopulation genetic algorithm, ant colony optimization, and a virus-based algorithm. TiM, a simulated environment for running distributed digital ecosystems, was used to perform the experiments. The results map the relation between the models and the expected object diversity obtained in short- and mid-term digital preservation scenarios. Comparing the results, the best performance corresponded to the multipopulation genetic algorithm. The article aims to be a first step in the digital self-preservation field. Building nature-inspired model behaviors is a good approach and opens the door to future tests with other AI-based methods.

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A Negotiation-Style Recommender Based on Computational Ecology in Open Negotiation Environments

Cited by 26 publications

References 17 publications

Recommender system application developments: A survey

Recommender system application developments: A survey

Digital business ecosystem: Literature review and a framework for future research

Analysis of Nature-Inspired Algorithms for Long-Term Digital Preservation

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