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

Maria Terzi, Matthew Rowe, Maria Angela Ferrario, Jon Whittle

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

8 Citations (Scopus)

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 Rotten Tomatoes and Audio CDs from Amazon Products. Our results show that the text-based user-kNN algorithm performs significantly better than the ratings-based approaches in terms of accuracy measured using RMSE.

Original languageEnglish
Title of host publicationUser Modeling, Adaptation, and Personalization
Subtitle of host publication22nd International Conference, UMAP 2014 Aalborg, Denmark, July 7-11, 2014 Proceedings
EditorsVania Dimitrova, Tsvi Kuflik, David Chin, Francesco Ricci, Peter Dolog, Geert-Jan Houben
Place of PublicationCham Switzerland
PublisherSpringer
Pages195-206
Number of pages12
ISBN (Electronic)9783319087856
ISBN (Print)9783319087863
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventInternational Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2014 - Aalborg, Netherlands
Duration: 7 Jul 201411 Jul 2014
Conference number: 22nd
https://link.springer.com/book/10.1007/978-3-319-08786-3 (Conference Proceedings)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume8538
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2014
Abbreviated titleUMAP 2014
CountryNetherlands
CityAalborg
Period7/07/1411/07/14
Internet address

Keywords

  • Collaborative Filtering
  • Recommender systems
  • Semantic similarity measures
  • Text reviews

Cite this