Social recommender systems for web 2.0 folksonomies

ABSTRACT

The rapidly increasing popularity of Web 2.0 knowledge and content sharing systems and growing amount of shared data make discovering relevant content and finding contacts a difficult enterprize. Typically, folksonomies provide a rich set of structures and social relationships that can be mined for a variety of recommendation purposes. In this paper we propose a formal model to characterize users, items, and annotations in Web 2.0 environments. Our objective is to construct social recommender systems that predict the utility of items, users, or groups based on the multi-dimensional social environment of a given user. Based on this model we introduce recommendation mechanisms for content sharing frameworks. Our comprehensive evaluation shows the viability of our approach and emphasizes the key role of social meta knowledge for constructing effective recommendations in Web 2.0 applications.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.0 General

Additional Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.0 General

General Terms:
Algorithms, Experimentation, Human Factors

Keywords:
folksonomy, social media, social recommenders, web 2.0