Exploiting user preference similarity transitivity in nearest neighbour recommender algorithms
(2015) FMS820 20152Mathematical 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)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/7988951
- author
- Malmberg, Johan and Öinert, Magnus
- supervisor
- organization
- course
- FMS820 20152
- year
- 2015
- 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}},
language = {{eng}},
note = {{Student Paper}},
title = {{Exploiting user preference similarity transitivity in nearest neighbour recommender algorithms}},
year = {{2015}},
}