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Text-based user-kNN: measuring user similarity based on text reviews

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Publication date23/06/2014
Host publicationUser modeling, adaptation, and personalization
PublisherSpringer
Pages195-206
Number of pages12
ISBN (Electronic)9783319087863
ISBN (Print)9783319087856
<mark>Original language</mark>English

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume8538
ISSN (Print)0302-9743

Abstract

This article reports on a modification of the user-kNN algorithm that measures the similarity between users based on the similarity of text reviews, instead of ratings. We investigate the performance of text semantic similarity measures and we evaluate our text-based user-kNN approach by comparing it to a range of ratings-based approaches in a ratings prediction task. We do so by using datasets from two different domains: movies from RottenTomatoes and Audio CDs from Amazon Products. Our results show that the text-based userkNN algorithm performs significantly better than the ratings-based approaches in terms of accuracy measured using RMSE.