Abstract
Collaborative recommender systems suggest items each user may like or find useful basing on the preferences of other like-minded individuals. Thus, the main concern in a collaborative recommendation is to identify the most suitable set of users to drive the selection of the items to be offered in each case. To distinguish relevant and reliable users from unreliable ones, trust and reputation models are being increasingly incorporated in these systems, by using network structures in which nodes represent users and edges represent trust statements. However, current approaches require the users to provide explicit data (about which other users they trust or not) to form such networks. In this chapter, we apply a semantic approach to automatically build implicit trust networks and, thereby, improve the recommendation results transparently to the users.
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Martín-Vicente, M.I., Gil-Solla, A., Ramos-Cabrer, M. (2012). Implicit Trust Networks: A Semantic Approach to Improve Collaborative Recommendations. In: Recommender Systems for the Social Web. Intelligent Systems Reference Library, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25694-3_5
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DOI: https://doi.org/10.1007/978-3-642-25694-3_5
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