July 2017
Intermediate to advanced
360 pages
8h 26m
English
Content preview from Machine Learning Algorithms
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Singular Value Decomposition strategy
The first approach is based on the Singular Value Decomposition (SVD) of the user-item matrix. This technique allows transforming a matrix through a low-rank factorization and can also be used in an incremental way as described in Sarwar B., Karypis G., Konstan J., Riedl J., Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems, 2002. In particular, if the user-item matrix has m rows and n columns:
We have assumed that we have real matrices (which is often true in our case), but, in general, they are complex. U and V are unitary, while sigma is diagonal. The columns ...
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