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Tuning of Learning Algorithms for Use in Automated Product Recommendations

Jäderbo, Jakob (2014) In Master's Theses in Mathematical Sciences FMS820 20141
Mathematical Statistics
Abstract (Swedish)
In this thesis, we study the problem of predicting users' media preferences based on their,
as well as other users' historical rating data. The model used is that a user's rating for
an item can be explained by a sum of bias terms and an inner product between two
vectors in some multidimensional feature space that is specic to the product domain.
This model is tted using a stochastic descent type method, the stochastic diagonal
Levenberg-Marquardt method. The method is studied with respect to its stability and
how fast it adapts its parameter estimates and it is found that these characteristics can
be accurately predicted for linear regression problems, but that the dynamics become
much more complex when training the nonlinear... (More)
In this thesis, we study the problem of predicting users' media preferences based on their,
as well as other users' historical rating data. The model used is that a user's rating for
an item can be explained by a sum of bias terms and an inner product between two
vectors in some multidimensional feature space that is specic to the product domain.
This model is tted using a stochastic descent type method, the stochastic diagonal
Levenberg-Marquardt method. The method is studied with respect to its stability and
how fast it adapts its parameter estimates and it is found that these characteristics can
be accurately predicted for linear regression problems, but that the dynamics become
much more complex when training the nonlinear features. The concept of regularization
and the tuning of regularization parameters with the Nelder-Mead simplex method is
also discussed, as well as some methods for making the Python implementation faster
by using Cython. (Less)
Please use this url to cite or link to this publication:
author
Jäderbo, Jakob
supervisor
organization
course
FMS820 20141
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMAS-3257-2014
ISSN
1404-6342
other publication id
2014:E40
language
English
id
4499301
date added to LUP
2014-06-23 15:01:46
date last changed
2024-10-14 15:08:17
@misc{4499301,
  abstract     = {{In this thesis, we study the problem of predicting users' media preferences based on their,
as well as other users' historical rating data. The model used is that a user's rating for
an item can be explained by a sum of bias terms and an inner product between two
vectors in some multidimensional feature space that is specic to the product domain.
This model is tted using a stochastic descent type method, the stochastic diagonal
Levenberg-Marquardt method. The method is studied with respect to its stability and
how fast it adapts its parameter estimates and it is found that these characteristics can
be accurately predicted for linear regression problems, but that the dynamics become
much more complex when training the nonlinear features. The concept of regularization
and the tuning of regularization parameters with the Nelder-Mead simplex method is
also discussed, as well as some methods for making the Python implementation faster
by using Cython.}},
  author       = {{Jäderbo, Jakob}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Tuning of Learning Algorithms for Use in Automated Product Recommendations}},
  year         = {{2014}},
}