A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering

Remigio Hurtado; Arantxa Muñoz

doi:10.1007/978-981-97-3556-3_21

A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering

Remigio Hurtado
, Arantxa Muñoz

Research Group on Artificial Intelligence and Assistive Technologies (GI-IATA)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Recommender systems help predict what customers might like, such as movies, restaurants, or products. Collaborative filtering, a crucial part of these systems, faces challenges when dealing with user, item, and rating data. Traditional machine learning struggles with this data because user and item data are categorical. To solve this, we propose a method that transforms the original data into new variables, making it more suitable for advanced machine learning and deep learning techniques. This approach enhances prediction quality and opens doors for innovative data processing methods in collaborative filtering.

Original language	English
Title of host publication	Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024
Editors	Xin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	261-271
Number of pages	11
ISBN (Print)	9789819735556
DOIs	https://doi.org/10.1007/978-981-97-3556-3_21
State	Published - 2024
Event	9th International Congress on Information and Communication Technology, ICICT 2024 - London, United Kingdom Duration: 19 Feb 2024 → 22 Feb 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1012 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	9th International Congress on Information and Communication Technology, ICICT 2024
Country/Territory	United Kingdom
City	London
Period	19/02/24 → 22/02/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Keywords

Categorical transformer
Collaborative filtering
Data science
Data transformation
Machine learning
Recommender systems

CACES Knowledge Areas

245A Statistics
8116A Information Systems

Access to Document

10.1007/978-981-97-3556-3_21

Cite this

Hurtado, R., & Muñoz, A. (2024). A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering. In X.-S. Yang, S. Sherratt, N. Dey, & A. Joshi (Eds.), Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024 (pp. 261-271). (Lecture Notes in Networks and Systems; Vol. 1012 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-3556-3_21

Hurtado, Remigio ; Muñoz, Arantxa. / A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering. Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. editor / Xin-She Yang ; Simon Sherratt ; Nilanjan Dey ; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2024. pp. 261-271 (Lecture Notes in Networks and Systems).

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abstract = "Recommender systems help predict what customers might like, such as movies, restaurants, or products. Collaborative filtering, a crucial part of these systems, faces challenges when dealing with user, item, and rating data. Traditional machine learning struggles with this data because user and item data are categorical. To solve this, we propose a method that transforms the original data into new variables, making it more suitable for advanced machine learning and deep learning techniques. This approach enhances prediction quality and opens doors for innovative data processing methods in collaborative filtering.",

keywords = "Categorical transformer, Collaborative filtering, Data science, Data transformation, Machine learning, Recommender systems",

author = "Remigio Hurtado and Arantxa Mu{\~n}oz",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 9th International Congress on Information and Communication Technology, ICICT 2024 ; Conference date: 19-02-2024 Through 22-02-2024",

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doi = "10.1007/978-981-97-3556-3\_21",

language = "English",

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series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

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Hurtado, R & Muñoz, A 2024, A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering. in X-S Yang, S Sherratt, N Dey & A Joshi (eds), Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. Lecture Notes in Networks and Systems, vol. 1012 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 261-271, 9th International Congress on Information and Communication Technology, ICICT 2024, London, United Kingdom, 19/02/24. https://doi.org/10.1007/978-981-97-3556-3_21

A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering. / Hurtado, Remigio; Muñoz, Arantxa.
Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. ed. / Xin-She Yang; Simon Sherratt; Nilanjan Dey; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2024. p. 261-271 (Lecture Notes in Networks and Systems; Vol. 1012 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering

AU - Hurtado, Remigio

AU - Muñoz, Arantxa

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - Recommender systems help predict what customers might like, such as movies, restaurants, or products. Collaborative filtering, a crucial part of these systems, faces challenges when dealing with user, item, and rating data. Traditional machine learning struggles with this data because user and item data are categorical. To solve this, we propose a method that transforms the original data into new variables, making it more suitable for advanced machine learning and deep learning techniques. This approach enhances prediction quality and opens doors for innovative data processing methods in collaborative filtering.

AB - Recommender systems help predict what customers might like, such as movies, restaurants, or products. Collaborative filtering, a crucial part of these systems, faces challenges when dealing with user, item, and rating data. Traditional machine learning struggles with this data because user and item data are categorical. To solve this, we propose a method that transforms the original data into new variables, making it more suitable for advanced machine learning and deep learning techniques. This approach enhances prediction quality and opens doors for innovative data processing methods in collaborative filtering.

KW - Categorical transformer

KW - Collaborative filtering

KW - Data science

KW - Data transformation

KW - Machine learning

KW - Recommender systems

UR - https://www.scopus.com/pages/publications/85201974382

U2 - 10.1007/978-981-97-3556-3_21

DO - 10.1007/978-981-97-3556-3_21

M3 - Conference contribution

AN - SCOPUS:85201974382

SN - 9789819735556

T3 - Lecture Notes in Networks and Systems

SP - 261

EP - 271

BT - Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024

A2 - Yang, Xin-She

A2 - Sherratt, Simon

A2 - Dey, Nilanjan

A2 - Joshi, Amit

PB - Springer Science and Business Media Deutschland GmbH

T2 - 9th International Congress on Information and Communication Technology, ICICT 2024

Y2 - 19 February 2024 through 22 February 2024

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Hurtado R, Muñoz A. A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering. In Yang XS, Sherratt S, Dey N, Joshi A, editors, Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. Springer Science and Business Media Deutschland GmbH. 2024. p. 261-271. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-97-3556-3_21

A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering

Abstract

Publication series

Conference

Bibliographical note

Keywords

CACES Knowledge Areas

Access to Document

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AI-EduResearch: Platform for Supporting Research and Learning Powered by Artificial Intelligence and Machine Learning Models

Cite this

A Categorical Transformer with a Data Science Approach for Recommendation Systems Based on Collaborative Filtering

Abstract

Publication series

Conference

Bibliographical note

Keywords

CACES Knowledge Areas

Access to Document

Other files and links

Projects

AI-EduResearch: Platform for Supporting Research and Learning Powered by Artificial Intelligence and Machine Learning Models

Cite this