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2018 | OriginalPaper | Chapter

Item-Based Vs User-Based Collaborative Recommendation Predictions

Author : Joel Azzopardi

Published in: Semantic Keyword-Based Search on Structured Data Sources

Publisher: Springer International Publishing

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Abstract

The use of personalised recommendation systems to push interesting items to users has become a necessity in the digital world that contains overwhelming amounts of information. One of the most effective ways to achieve this is by considering the opinions of other similar users – i.e. through collaborative techniques. In this paper, we compare the performance of item-based and user-based recommendation algorithms as well as propose an ensemble that combines both systems. We investigate the effect of applying LSA, as well as varying the neighbourhood size on the different algorithms. Finally, we experiment with the inclusion of content-type information in our recommender systems. We find that the most effective system is the ensemble system that uses LSA.

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Title: Item-Based Vs User-Based Collaborative Recommendation Predictions
Author: Joel Azzopardi
Publisher: Springer International Publishing
Book: Semantic Keyword-Based Search on Structured Data Sources
Print ISBN: 978-3-319-74496-4

Electronic ISBN: 978-3-319-74497-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-74497-1_16

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