During the past decade, online trust and reputation systems have provided cogent answers to emerging challenges in the global computing infrastructures relating to computer and network security, electronic commerce, virtual enterprises, social networks and cloud computing. The goal of these systems in such global computing infrastructures is to allow entities to reason about the trustworthiness of other entities and to make autonomous decisions on the basis of trust. This requires the development of computational trust models that enable entities to reason about trust and to verify the properties of a particular interaction. The robustness of these mechanisms is one of the critical factors required for the success of this technology. In this paper, we briefly present characteristics of existing online trust and reputation models and systems through a multidimensional framework that can serve as a basis to understand the current state of the art in the area. The critical open challenges that limit the effectiveness of today's trust and reputation systems are discussed by providing a comprehensive literature review. Furthermore, we present a set of our contributions as a way to address some of these challenges.
Abstract.A network of people having established trust relations and a model for propagation of related trust scores are fundamental building blocks in many of todays most successful e-commerce and recommendation systems. However, the web of trust is often too sparse to predict trust values between non-familiar people with high accuracy. Trust inferences are transitive associations among users in the context of an underlying social network and may provide additional information to alleviate the consequences of the sparsity and possible cold-start problems. Such approaches are helpful, provided that a complete trust path exists between the two users. An alternative approach to the problem is advocated in this paper. Based on collaborative filtering one can exploit the like-mindedness resp. similarity of individuals to infer trust to yet unknown parties which increases the trust relations in the web. For instance, if one knows that with respect to a specific property, two parties are trusted alike by a large number of different trusters, one can assume that they are similar. Thus, if one has a certain degree of trust to the one party, one can safely assume a very similar trustworthiness of the other one. In an attempt to provide high quality recommendations and proper initial trust values even when no complete trust propagation path or user profile exists, we propose TILLIT -a model based on combination of trust inferences and user similarity. The similarity is derived from the structure of the trust graph and users' trust behavior as opposed to other collaborative-filtering based approaches which use ratings of items or user's profile. We describe an algorithm realizing the approach based on a combination of trust inferences and user similarity, and validate the algorithm using a real large-scale data-set.
Recommending news articles entails additional requirements to recommender systems. Such requirements include special consumption patterns, fluctuating item collections, and highly sparse user profiles. This workshop (NRS'13@RecSys) brought together researchers and practitioners around the topics of designing and evaluating novel news recommender systems. Additionally, we offered a challenge allowing participants to evaluate their recommendation algorithms with actual user feedback.
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