Abstract
Collaborative filtering has been widely used to predict the interests of a user. Given a users past activities, collaborative filtering predicts the users future preferences. This talk presents techniques and discoveries of our recent parallelization effort on collaborative filtering algorithms. In particular, parallel association mining and parallel latent Dirichlet allocation will be presented and their pros and cons analyzed. Some counter-intuitive results will also be presented to stimulate future parallel optimization research.
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© 2009 Springer-Verlag Berlin Heidelberg
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Chang, E.Y. (2009). Parallel Algorithms for Collaborative Filtering. In: Goldberg, A.V., Zhou, Y. (eds) Algorithmic Aspects in Information and Management. AAIM 2009. Lecture Notes in Computer Science, vol 5564. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02158-9_2
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DOI: https://doi.org/10.1007/978-3-642-02158-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02157-2
Online ISBN: 978-3-642-02158-9
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