Opinion-Based Filtering through Trust

Abstract. Multi-agent systems are known for their ability to adapt quickly and effectively to changes in their environment. This work proposes a model for the development of digital content retrieval based on the paradigm of virtual organizations of agents. The model allows the development of an open and flexible architecture that supports the services necessary to conduct a search for distributed digital content dynamically. AIREH (Architecture for Intelligent Recovery of Educational content in Heterogeneous Environments) is based on the proposed model; it is a multi-agent architecture that can search and integrate heterogeneous educational content through a recovery model that uses a federated search. A major challenge in searching and retrieval digital content is to efficiently find the most suitable for the users This paper proposes a new approach to filter the educational content retrieved based on Case-Based Reasoning (CBR).The model and the technologies presented in this research are an example of the potential for developing recovery systems for digital content based on the paradigm of virtual organizations of agents. The advantages of the proposed architecture are its flexibility, customization, integrative solution and efficiency.

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“…Recovery techniques and their adaptation to CBR techniques have become effective for the development of recommender systems [17].…”

Section: Proposed Recommendation Strategymentioning

confidence: 99%

Intelligent Recovery Architecture for Personalized Educational Content

Gil

Rodrı́guez

Prieta

et al. 2012

Advances in Intelligent and Soft Computing

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“…See [5] for further details. The current querying agent, aq, gathers a total of n interest values of each enquired agent ae, one for each product in the training set.…”

Section: Social Trust Model For Recom-mender Agentsmentioning

confidence: 99%

Developing trust in recommender agents

Montaner¹,

López²,

Rosa³

2002

Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems Part 1 - AAMAS '02

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Trust is one of the most important social concepts that helps human agents to cope with their social environment and is present in all human interaction. Like in real world, agents should rely in some agents and mistrust in other ones to achieve a purpose. In this paper we develop a model of trust in the collaborative world as a new approach of recommender agents development. Mainly, we provide recommender agents with a technology to look for similar agents that advice him. The model presented comprehends the evolution of trust, that is, trust dynamics. Moreover, the model proposed emphasizes proactiveness since the agent looks for other agents in a lack for information situation instead of remaining passive or providing either a negative or empty answer to the user.

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“…Montaner et al propose a trust model, introduce the concept of trust factor [3]. They believe that the trust factor is dynamically changed along with the users' satisfaction.…”

Section: Introductionmentioning

confidence: 99%

A Trusted Recommendation Method Based on Social Circles

Cheng-jian¹,

Chen²,

Song³

2016

Proceedings of the 2016 International Conference on Computer Science and Electronic Technology

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Abstract:The crisis of confidence has become increasingly rigorous, especially in recommendation domain. Deception and malicious attacks are found among the traditional collaborative filtering recommendation systems. Taking into account the credibility, the behaviour-based trust relationship, and the social circle-based trust relationship, this paper proposes a trusted recommendation method via social circle to enhance the credibility of recommendation. By using the social circles, which can significantly help to deal with the sparsity and trust problem of the traditional collaborative filtering, the method is aimed to solve two fundamental problems: accuracy and robustness. Empirical evaluation of the method on the dataset of last.fm demonstrates that the method can enhance the robustness without reducing the accuracy of the recommendation.

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Opinion-Based Filtering through Trust

Cited by 54 publications

References 10 publications

Intelligent Recovery Architecture for Personalized Educational Content

Intelligent Recovery Architecture for Personalized Educational Content

Developing trust in recommender agents

A Trusted Recommendation Method Based on Social Circles

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