Data personalization on the open Web
Libs 150 | Computer Science homework help
Data personalization on the open Web is a much discussed topic. Any time we access content on the open Web our data choices are tracked, captured, and used for various purposes. Watch this nine minute video and consider the implications of algorithmic “filter bubbles”.
1. How might machine filtering affect your research results?
2. How might these computer algorithms determine what you see about presidential candidates?
Post your thoughts in at least 100 words and then reply to at least one peer.
Filter Bubbles by Eli Pariser
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useful sources
Bouzeghoub, M., & Kostadinov, D. (2007). Data personalization: a taxonomy of user profiles knowledge and a profile management tool. Rapports de recherche du laboratoire PRiSM,[en ligne] http://www. prism. uvsq. fr/rapports/bin/bibliography. php.
Koutrika, G. (2015). Data personalization. In Data Management in Pervasive Systems (pp. 213-234). Springer, Cham.
Guzella, T. S., & Caminhas, W. M. (2009). A review of machine learning approaches to spam filtering. Expert Systems with Applications, 36(7), 10206-10222.
Carron, A., Todescato, M., Carli, R., Schenato, L., & Pillonetto, G. (2016, December). Machine learning meets Kalman filtering. In 2016 IEEE 55th conference on decision and control (CDC) (pp. 4594-4599). IEEE.
