Trust-based Recommender Systems Research Articles

English

In Army Services, there are a number of valuable decisions that have to be taken for mission accomplishment. These decisions are very important and the choice of a weapon may be able to alter the outcome of a battle decisively. Among several such decisions one is to decide which weapons to deploy/assign over a given terrain. Recommender systems are intelligent applications to assist users in a decision-making process where they want to choose one item amongst a potentially overwhelming set of alternative products or services. This paper proposes the design of recommender system that automates the process of finding the appropriate type of weapon(s) that can be deployed over a terrain having certain characteristics. The user agent seeks recommendations, which are in the form of intuitionistic fuzzy set (IFS), from trustworthy peers and produces aggregated order of recommendations taking degree of trust on recommenders into consideration. Trust on recommender is also updated based on importance of recommendation given to the user. A prototype of the trust-based recommender system for modern tactical combat system has been designed and developed through which the user can get the recommendation to use a specific kind of weapon or a set of weapons that would be best-suited in a given type of terrain. Defence Science Journal, 2010, 60(4), pp.405-411 , DOI:http://dx.doi.org/10.14429/dsj.60.498

TREPPS: A Trust-based Recommender System for Peer Production Services

Peer production, a new mode of production, is gradually shifting the traditional, capital-intensive wealth production to a model which heavily depends on information creating and sharing. More and more online users are relying on this type of services such as news, articles, bookmarks, and various user-generated contents around World Wide Web. However, the quality and the veracity of peers’ contributions are not well managed. Without a practical means to assess the quality of peer production services, the consequence is information-overloading. In this study, we present a recommender system based on the trust of social networks. Through the trust computing, the quality and the veracity of peer production services can be appropriately assessed. Two prominent fuzzy logic applications – fuzzy inference system and fuzzy MCDM method are utilized to support the decision of service choice. The experimental results showed that the proposed recommender system can significantly enhance the quality of peer production services and furthermore overcome the information overload problems. In addition, a trust-based social news system is built to demonstrate the application of the proposed system.

A model of a trust-based recommendation system on a social network

In this paper, we present a model of a trust-based recommendation system on a social network. The idea of the model is that agents use their social network to reach information and their trust relationships to filter it. We investigate how the dynamics of trust among agents affect the performance of the system by comparing it to a frequency-based recommendation system. Furthermore, we identify the impact of network density, preference heterogeneity among agents, and knowledge sparseness to be crucial factors for the performance of the system. The system self-organises in a state with performance near to the optimum; the performance on the global level is an emergent property of the system, achieved without explicit coordination from the local interactions of agents.

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