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A Random Indexing Approach to User Preference Prediction
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Predicting user preferences is a common problem for many companies and

services. With the growth of Internet services it becomes both more important

and more lucrative being able to predict what products a user would like and

then recommend these to them. There are many ways of attempting this,

but this study attempts to use random indexing to solve the same problem.

Random indexing is a method that has been used successfully when studying

the similarity between words, and allows entities to be represented as vectors

with relatively small dimensionality. This would allow for fast and memory-efficient implementations of prediction systems.

This study uses the dataset Amazon Fine Food Reviews, which contains

reviews of products with a rating. It is attempted to predict these ratings, and

the result of random indexing is compared to the results on the same dataset

using collaborative filtering. Various parameters used in the random indexing

method are also varied, to study their effect on the results. These methods are

evaluated based on root mean square error and mean absolute error.

The results indicate that random indexing does not generate as good results

as collaborative filtering. However, the difference is small enough to warrant

further study into the other strengths of random indexing, such as speed and

memory efficiency. It is theorized that the sparsity of the dataset might have

caused the differences in errors between the methods, and with a dense dataset

the results might be better.

Place, publisher, year, edition, pages
National Category
Computer Science
URN: urn:nbn:se:kth:diva-186276OAI: diva2:926709
Available from: 2016-05-18 Created: 2016-05-09 Last updated: 2016-05-18Bibliographically approved

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