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Exploiting user preference similarity transitivity in nearest neighbour recommender algorithms

Malmberg, Johan and Öinert, Magnus (2015) FMS820 20152
Mathematical Statistics
Abstract
This thesis explores the Nearest Neighbour recommender algorithm and a
proposed way of recomputing the elements of a set, containing values from
a similarity metric. In essence, this extends the neighbourhood used in the
algorithm by making use of close neighbours’ neighbours. This proposal is
motivated by the hypothesis that high user preference similarity is transitive.
A programme was implemented in Matlab, and one of the MovieLens data
sets was used. Three experiments were run. These indicated that further exploration
of this property and the proposed methods could be of interest. The
final experiment was run on the simplest form of a Nearest Neighbour recommender,
with the added re-computational step, and suggests that there... (More)
This thesis explores the Nearest Neighbour recommender algorithm and a
proposed way of recomputing the elements of a set, containing values from
a similarity metric. In essence, this extends the neighbourhood used in the
algorithm by making use of close neighbours’ neighbours. This proposal is
motivated by the hypothesis that high user preference similarity is transitive.
A programme was implemented in Matlab, and one of the MovieLens data
sets was used. Three experiments were run. These indicated that further exploration
of this property and the proposed methods could be of interest. The
final experiment was run on the simplest form of a Nearest Neighbour recommender,
with the added re-computational step, and suggests that there is a
small, but statistically significant, improvement due to this new step (for one
of the proposed methods) confirming the hypothesis. The improvement of the
errors of estimated rating predictions were in the order of magnitude 0.1 %. (Less)
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author
Malmberg, Johan and Öinert, Magnus
supervisor
organization
course
FMS820 20152
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Recommender systems, Recommender algorithms, kNN, Nearest Neighbour, MovieLens, Collaborative Filtering.
language
English
id
7988951
date added to LUP
2015-09-23 11:32:17
date last changed
2015-09-23 11:32:17
@misc{7988951,
  abstract     = {This thesis explores the Nearest Neighbour recommender algorithm and a
proposed way of recomputing the elements of a set, containing values from
a similarity metric. In essence, this extends the neighbourhood used in the
algorithm by making use of close neighbours’ neighbours. This proposal is
motivated by the hypothesis that high user preference similarity is transitive.
A programme was implemented in Matlab, and one of the MovieLens data
sets was used. Three experiments were run. These indicated that further exploration
of this property and the proposed methods could be of interest. The
final experiment was run on the simplest form of a Nearest Neighbour recommender,
with the added re-computational step, and suggests that there is a
small, but statistically significant, improvement due to this new step (for one
of the proposed methods) confirming the hypothesis. The improvement of the
errors of estimated rating predictions were in the order of magnitude 0.1 %.},
  author       = {Malmberg, Johan and Öinert, Magnus},
  keyword      = {Recommender systems,Recommender algorithms,kNN,Nearest Neighbour,MovieLens,Collaborative Filtering.},
  language     = {eng},
  note         = {Student Paper},
  title        = {Exploiting user preference similarity transitivity in nearest neighbour recommender algorithms},
  year         = {2015},
}