Abstract
User modeling systems have been influenced by the overspread of Web 2.0 and social networks. New systems aimed at helping people finding information of interest and including “social functions” like social networks, tagging, commenting, inserting content, arose. Such systems are the so-called “social recommender systems”. The idea at the base of social recommender systems is that the recommendation of content should follow user’s preferences while social network just represents a group of users joined by some kind of voluntary relation and does not reflect any preference. We claim that social network is a very important source of information to profile users. Moving from theories in social psychology which describe influence dynamics among individuals, we state that joining in a network with other people exposes individuals to social dynamics which can influence their attitudes, behaviours and preferences.
We present in this paper SoNARS, a new algorithm for recommending content in social recommender systems. SoNARS targets users as members of social networks, suggesting items that reflect the trend of the network itself, based on its structure and on the influence relationships among users.
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Carmagnola, F., Vernero, F., Grillo, P. (2009). SoNARS: A Social Networks-Based Algorithm for Social Recommender Systems. In: Houben, GJ., McCalla, G., Pianesi, F., Zancanaro, M. (eds) User Modeling, Adaptation, and Personalization. UMAP 2009. Lecture Notes in Computer Science, vol 5535. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02247-0_22
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DOI: https://doi.org/10.1007/978-3-642-02247-0_22
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