Past evidence has shown that generic approaches to recommander systems based upon collaborative filtering tend to poorly scale. Moreover, their fitness for scenarios supposing distributed data storage and decentralized control, like the Semantic Web, becomes largely limited for various reasons. We believe that computational trust models bear several favorable properties for social filtering, opening new opportunities by either replacing or supplementing current techniques. However, in order to provide meaningful results for recommender system applications, we expect notions of trust to clearly reflect user similarity. In this work, we therefore provide empirical results obtained from one real, operational community and verify latter hypothesis for the domain of book recommendations. © Springer-Verlag 2004.
CITATION STYLE
Ziegler, C. N., & Lausen, G. (2004). Analyzing correlation between trust and user similarity in online communities. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2995, 251–265. https://doi.org/10.1007/978-3-540-24747-0_19
Mendeley helps you to discover research relevant for your work.