Proof-of-Concept of a Trust-based Recommender System
This project is related to our research lines: Systemic Risk and Financial networks Design and analysis of socio-technical systems
Duration 18 months (May 2008 - October 2009)
Funding source MTEC Foundation, Stiftung zur Förderung der Forschung und Ausbildung in Unternehmenswissenschaften an der ETH Zürich
Recommender Systems (RS) are applications that enable users of a particular online platform, e.g. Amazon, Last.FM, etc., to retrieve information on products and services offered. This information can be provided at different levels of personalisation and filtering. Thus, RS can be seen as tools to support the decision-making of consumers; because of this, they have become more and more widespread in all economic sectors. This project extends a novel type of electronic RS, developed at our chair, towards a real-world application. Differently from existing RS, the proposed system leverages the fact that users are part of a real social network and that they trust each other to different extents depending on the context. The main benefit of this approach is that it offers personalisation i.e. the recommendations is tailored to each individual user.
The main objective of the project is to prove the feasibility of a trust-based recommender system using social networks. We achieved this goal by developing a Knowledge Sharing Playground (KSP) which is, at a glance, a web application where users can share knowledge with other users. The framework implements the trust algorithms presented in Walter et al. (2006) and extended in Walter et al. (2009).
Currently, our group is negotiating a partnership with development companies in order to continue the development of the framework and to migrate it towards a commercial application. This will allow us to collect real data and validate empirically our models of RS.