The information overflow of today’s information society can be overcome by the usage of recommender systems. Due to the fact that most recommender systems act as black boxes, trust in a system decrease, especially when a recommendation failed. Recommender systems usually don’t offer any insight into the systems logic and cannot be questioned as it is normal for a recommendation process between ...

Trust networks among users of a recommender system (RS) prove beneficial to the quality and amount of the recommendations. Since trust is often a gradual phenomenon, fuzzy relations are the pre-eminent tools for modeling such networks. However, as current trust-enhanced RSs do not work with the notion of distrust, they cannot differentiate unknown users from malicious users, nor represent incon...

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’ con...

In a growing number of domains, recommender systems support users’ decision making processes in finding entities (such as book, movies, or newspaper articles) matching their individual preferences. Individual preferences vary extensively thus hampering the creation of good recommender systems. Additionally, the relevance of items to users depends on a plethora of criteria. Such relevance criter...

In this paper, we analyse the sustainability of social networks using STrust, our social trust model. The novelty of the model is that it introduces the concept of engagement trust and combines it with the popularity trust to derive the social trust of the community as well as of individual members in the community. This enables the recommender system to use these different types of trust to re...

Recently, there has been growing interest in recommender systems (RSs) and particularly in context-aware RSs. Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper focuses on comparing the pre-filtering, the post-filtering, the contextual modeling and the un-contextual approaches and on identify...

The goal of the work presented in this paper is to design a context-aware recommender system for mobile devices. The approach is based on decentralized, item-based collaborative filtering on Personal Digital Assistants (PDAs). The already implemented system exchanges rating vectors among PDAs, computes local matrices of item similarity and utilizes them to generate recommendations. We then expl...

Exploring new applications and services for mobile environments has generated considerable excitement among both commercial companies and academics. In this paper we propose a location-aware recommender system that accommodates a customer’s shopping needs with location-dependent vendor offers and promotions. Specifically, we propose a recommender system for recommending vendors’ webpages – incl...

With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring these applications to user-specific needs or preferences. Despite their increasing popularity, in general recommender systems suffer from the data sparsity and the cold-start problems. To alleviate these issues, in re...