Recommender systems are frequently used as part of online shops to help consumers browse through large product offerings by recommending those products which are the most relevant for them. Although consumers’ interactions with recommender systems have been subject to substantial research, it is still unclear what the effect on aggregated sales diversity is, i.e. whether this leads to predomina...

Recommender systems are now mainstream and people are increasingly relying on them to make decisions in situations where there are too many options to choose from. Yet many recommender systems act like “black boxes”, providing little or no transparency into the rationale of their recommendation process [1]. Related research in the field of recommender systems has focused on developing and evalu...

Recommender systems have become increasingly popular. Most of the research on recommender systems has focused on recommendation algorithms. There has been relatively little research, however, in the area of generalized system architectures for recommendation systems. In this paper, we introduce weHelp: a reference architecture for social recommender systems - systems where recommendations are d...

In this paper, we argue that the process of developing travel recommender systems (TRS) can be simplified. By studying the application domain of tourism information systems, and examining the algorithms and architectures available for recommender systems today, we discuss the dependencies and present a methodology for developing TRS, which can be applied at very early stages of TRS development....

Recommender systems are acknowledged as an essential instrument to support users in finding relevant information. However, the adaptation of recommender systems to multiple domain-specific requirements and data models still remains an open challenge. In the present paper, we contribute to this sparse line of research with guidance on how to design a customizable recommender system that accounts...