Abstract
This paper addresses the first stage of a Friend-based Advertisement Recommendation System. Our system operates in the environment of social networks like MySpace and Facebook. The goal of the system is to use social data from a user and their friends to make peer-pressure based advertisement recommendations. Gleaning this social information from the user and their pre-chosen set of friends is the focus of this paper. We discuss what this data is, how it is obtained and most importantly how we “quantize” it into numerical information that can be further processed for use in our recommendation system. Different techniques including linguistic as well as web services are explored. Results on real social data are given.
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Grewe, L., Pandey, S. (2011). Quantization of Social Data for Friend Advertisement Recommendation System. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Advances in Parallel Distributed Computing. PDCTA 2011. Communications in Computer and Information Science, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24037-9_59
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DOI: https://doi.org/10.1007/978-3-642-24037-9_59
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