Abstract
Privacy problem is a big challenge in collaborative systems. Such systems depend on users collected data to generate recommendations in their future visits. Site visitors give falsify information to avoid privacy disclosure; this leads to inefficient recommendations. In this paper, we address the privacy problem in collaborative systems; we suggested a new perceiving intellectual style to generate recommendations and avoiding users’ privacy issues. According to the suggested approach, we were able to provide two types of recommendations, the Intellectual Node Recommendation or the Intellectual Batch Recommendation. We evaluated both recommendation types by calculating levels of coverage and precision. We found that Intellectual Batch Recommendation achieved better performance comparing to the Intellectual Node Recommendation.
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Embarak, O., Saeed, K., Ali, M. (2019). Perceiving Intellectual Style to Solve Privacy Problem in Collaborative Systems. In: Barolli, L., Xhafa, F., Khan, Z., Odhabi, H. (eds) Advances in Internet, Data and Web Technologies. EIDWT 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-12839-5_37
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DOI: https://doi.org/10.1007/978-3-030-12839-5_37
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