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

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

doi:10.1007/978-3-319-08786-3_17

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 proceeding › Conference Paper › Research

13 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 language	English
Title of host publication	User Modeling, Adaptation, and Personalization
Subtitle of host publication	22nd International Conference, UMAP 2014 Aalborg, Denmark, July 7-11, 2014 Proceedings
Editors	Vania Dimitrova, Tsvi Kuflik, David Chin, Francesco Ricci, Peter Dolog, Geert-Jan Houben
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	195-206
Number of pages	12
ISBN (Electronic)	9783319087856
ISBN (Print)	9783319087863
DOIs	https://doi.org/10.1007/978-3-319-08786-3_17
Publication status	Published - 2014
Externally published	Yes
Event	International Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2014 - Aalborg, Netherlands Duration: 7 Jul 2014 → 11 Jul 2014 Conference number: 22nd https://link.springer.com/book/10.1007/978-3-319-08786-3 (Conference Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	8538
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2014
Abbreviated title	UMAP 2014
Country/Territory	Netherlands
City	Aalborg
Period	7/07/14 → 11/07/14
Internet address	https://link.springer.com/book/10.1007/978-3-319-08786-3 (Conference Proceedings)

Keywords

Collaborative Filtering
Recommender systems
Semantic similarity measures
Text reviews

Access to Document

10.1007/978-3-319-08786-3_17

Cite this

Terzi, M., Rowe, M., Ferrario, M. A., & Whittle, J. (2014). Text-based user-kNN: measuring user similarity based on text reviews. In V. Dimitrova, T. Kuflik, D. Chin, F. Ricci, P. Dolog, & G.-J. Houben (Eds.), User Modeling, Adaptation, and Personalization: 22nd International Conference, UMAP 2014 Aalborg, Denmark, July 7-11, 2014 Proceedings (pp. 195-206). (Lecture Notes in Computer Science ; Vol. 8538). Springer. https://doi.org/10.1007/978-3-319-08786-3_17

Terzi, Maria ; Rowe, Matthew ; Ferrario, Maria Angela et al. / Text-based user-kNN : measuring user similarity based on text reviews. User Modeling, Adaptation, and Personalization: 22nd International Conference, UMAP 2014 Aalborg, Denmark, July 7-11, 2014 Proceedings. editor / Vania Dimitrova ; Tsvi Kuflik ; David Chin ; Francesco Ricci ; Peter Dolog ; Geert-Jan Houben. Cham Switzerland : Springer, 2014. pp. 195-206 (Lecture Notes in Computer Science ).

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title = "Text-based user-kNN: measuring user similarity based on text reviews",

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.",

keywords = "Collaborative Filtering, Recommender systems, Semantic similarity measures, Text reviews",

author = "Maria Terzi and Matthew Rowe and Ferrario, {Maria Angela} and Jon Whittle",

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doi = "10.1007/978-3-319-08786-3_17",

language = "English",

isbn = "9783319087863",

series = "Lecture Notes in Computer Science ",

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pages = "195--206",

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booktitle = "User Modeling, Adaptation, and Personalization",

address = "Switzerland",

note = "International Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2014, UMAP 2014 ; Conference date: 07-07-2014 Through 11-07-2014",

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Terzi, M, Rowe, M, Ferrario, MA & Whittle, J 2014, Text-based user-kNN: measuring user similarity based on text reviews. in V Dimitrova, T Kuflik, D Chin, F Ricci, P Dolog & G-J Houben (eds), User Modeling, Adaptation, and Personalization: 22nd International Conference, UMAP 2014 Aalborg, Denmark, July 7-11, 2014 Proceedings. Lecture Notes in Computer Science , vol. 8538, Springer, Cham Switzerland, pp. 195-206, International Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2014, Aalborg, Netherlands, 7/07/14. https://doi.org/10.1007/978-3-319-08786-3_17

Text-based user-kNN: measuring user similarity based on text reviews. / Terzi, Maria; Rowe, Matthew; Ferrario, Maria Angela et al.
User Modeling, Adaptation, and Personalization: 22nd International Conference, UMAP 2014 Aalborg, Denmark, July 7-11, 2014 Proceedings. ed. / Vania Dimitrova; Tsvi Kuflik; David Chin; Francesco Ricci; Peter Dolog; Geert-Jan Houben. Cham Switzerland: Springer, 2014. p. 195-206 (Lecture Notes in Computer Science ; Vol. 8538).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

TY - GEN

T1 - Text-based user-kNN

T2 - International Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2014

AU - Terzi, Maria

AU - Rowe, Matthew

AU - Ferrario, Maria Angela

AU - Whittle, Jon

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PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Collaborative Filtering

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A2 - Kuflik, Tsvi

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Terzi M, Rowe M, Ferrario MA, Whittle J. Text-based user-kNN: measuring user similarity based on text reviews. In Dimitrova V, Kuflik T, Chin D, Ricci F, Dolog P, Houben GJ, editors, User Modeling, Adaptation, and Personalization: 22nd International Conference, UMAP 2014 Aalborg, Denmark, July 7-11, 2014 Proceedings. Cham Switzerland: Springer. 2014. p. 195-206. (Lecture Notes in Computer Science ). doi: 10.1007/978-3-319-08786-3_17

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

Abstract

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Conference

Keywords

Access to Document

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