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- Academic -- UNF -- Computing; music; collaborative filtering; collective intelligence; content-based filtering; MFCC; recommend; information retrieval; database; Pandora; Last.fm; music recommendation system; UNF (1)
- Academic -- UNF -- Master of Science in Computer and Information Sciences; Dissertations (1)
- Thesis; University of North Florida; UNF; Dissertations (1)
- Thesis; University of North Florida; UNF; Dissertations; Academic -- UNF -- Master of Science in Computer and Information Sciences; Dissertations; Academic -- UNF -- Computing; cloud; database; security; algorithm (1)
Articles 1 - 2 of 2
Full-Text Articles in Databases and Information Systems
A Comparison Of Cloud Computing Database Security Algorithms, Joseph A. Hoeppner
A Comparison Of Cloud Computing Database Security Algorithms, Joseph A. Hoeppner
UNF Graduate Theses and Dissertations
The cloud database is a relatively new type of distributed database that allows companies and individuals to purchase computing time and memory from a vendor. This allows a user to only pay for the resources they use, which saves them both time and money. While the cloud in general can solve problems that have previously been too costly or time-intensive, it also opens the door to new security problems because of its distributed nature. Several approaches have been proposed to increase the security of cloud databases, though each seems to fall short in one area or another.
This thesis presents …
A Hybrid Approach To Music Recommendation: Exploiting Collaborative Music Tags And Acoustic Features, Jaime C. Kaufman
A Hybrid Approach To Music Recommendation: Exploiting Collaborative Music Tags And Acoustic Features, Jaime C. Kaufman
UNF Graduate Theses and Dissertations
Recommendation systems make it easier for an individual to navigate through large datasets by recommending information relevant to the user. Companies such as Facebook, LinkedIn, Twitter, Netflix, Amazon, Pandora, and others utilize these types of systems in order to increase revenue by providing personalized recommendations. Recommendation systems generally use one of the two techniques: collaborative filtering (i.e., collective intelligence) and content-based filtering.
Systems using collaborative filtering recommend items based on a community of users, their preferences, and their browsing or shopping behavior. Examples include Netflix, Amazon shopping, and Last.fm. This approach has been proven effective due to increased popularity, and …