Home > Research > Publications & Outputs > Text-based user-kNN

Research

Associated organisational unit

Electronic data

Links

Text available via DOI:

View graph of relations

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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

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. Springer, 2014. p. 195-206 (Lecture Notes in Computer Science; Vol. 8538).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Terzi, M, Rowe, M, Ferrario, MA & Whittle, J 2014, Text-based user-kNN: measuring user similarity based on text reviews. in User modeling, adaptation, and personalization. Lecture Notes in Computer Science, vol. 8538, Springer, pp. 195-206. https://doi.org/10.1007/978-3-319-08786-3_17

APA

Terzi, M., Rowe, M., Ferrario, M. A., & Whittle, J. (2014). Text-based user-kNN: measuring user similarity based on text reviews. In User modeling, adaptation, and personalization (pp. 195-206). (Lecture Notes in Computer Science; Vol. 8538). Springer. https://doi.org/10.1007/978-3-319-08786-3_17

Vancouver

Terzi M, Rowe M, Ferrario MA, Whittle J. Text-based user-kNN: measuring user similarity based on text reviews. In User modeling, adaptation, and personalization. Springer. 2014. p. 195-206. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-08786-3_17

Author

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. Springer, 2014. pp. 195-206 (Lecture Notes in Computer Science).

Bibtex

@inproceedings{6d63be6140d4480aa9b9f0a1230dfd12,
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 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.",
author = "Maria Terzi and Matthew Rowe and Ferrario, {Maria Angela} and Jon Whittle",
year = "2014",
month = jun,
day = "23",
doi = "10.1007/978-3-319-08786-3_17",
language = "English",
isbn = "9783319087856",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "195--206",
booktitle = "User modeling, adaptation, and personalization",

}

RIS

TY - GEN

T1 - Text-based user-kNN

T2 - measuring user similarity based on text reviews

AU - Terzi, Maria

AU - Rowe, Matthew

AU - Ferrario, Maria Angela

AU - Whittle, Jon

PY - 2014/6/23

Y1 - 2014/6/23

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 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.

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 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.

U2 - 10.1007/978-3-319-08786-3_17

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

M3 - Conference contribution/Paper

SN - 9783319087856

T3 - Lecture Notes in Computer Science

SP - 195

EP - 206

BT - User modeling, adaptation, and personalization

PB - Springer

ER -