Enhancing Semantics-Driven Recommender Systems with Visual Features

Mounir M. Bendouch; Flavius Frasincar; Tarmo Robal

doi:10.1007/978-3-031-07472-1_26

Enhancing Semantics-Driven Recommender Systems with Visual Features

Mounir M. Bendouch, Flavius Frasincar, Tarmo Robal^*

^*Corresponding author for this work

Econometrics

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

Abstract

Content-based semantics-driven recommender systems are often used in the small-scale news recommendation domain, founded on the TF-IDF measure but also taking into account domain semantics through semantic lexicons or ontologies. This work explores the application of content-based semantics-driven recommender systems to large-scale recommendations on the example of movie domain. We propose methods to extract semantic features from various item descriptions, including images. In particular, we use computer vision to extract semantic features from images and use these for recommendation together with various features extracted from textual information. The semantics-driven approach is scaled up with pre-computation of the cosine similarities and gradient learning of the model. The results of the study on a large-scale MovieLens dataset of user ratings demonstrate that semantics-driven recommenders can be extended to more complex domains and outperform TF-IDF on ROC, PR, F₁, and Kappa metrics.

Original language	English
Title of host publication	Advanced Information Systems Engineering (caise 2022)
Editors	Xavier Franch, Geert Poels, Frederik Gailly, Monique Snoeck
Publisher	Springer Science+Business Media
Pages	443-459
Number of pages	17
ISBN (Print)	9783031074714
DOIs	https://doi.org/10.1007/978-3-031-07472-1_26
Publication status	Published - 2022
Event	34th International Conference on Advanced Information Systems Engineering, CAiSE 2022 - Leuven, Belgium Duration: 6 Jun 2022 → 10 Jun 2022

Publication series

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13295 LNCS
ISSN	0302-9743

Conference

Conference	34th International Conference on Advanced Information Systems Engineering, CAiSE 2022
Country/Territory	Belgium
City	Leuven
Period	6/06/22 → 10/06/22

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Access to Document

10.1007/978-3-031-07472-1_26

Cite this

Bendouch, M. M., Frasincar, F., & Robal, T. (2022). Enhancing Semantics-Driven Recommender Systems with Visual Features. In X. Franch, G. Poels, F. Gailly, & M. Snoeck (Eds.), Advanced Information Systems Engineering (caise 2022) (pp. 443-459). Springer Science+Business Media. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13295 LNCS https://doi.org/10.1007/978-3-031-07472-1_26

Bendouch, Mounir M. ; Frasincar, Flavius ; Robal, Tarmo. / Enhancing Semantics-Driven Recommender Systems with Visual Features. Advanced Information Systems Engineering (caise 2022). editor / Xavier Franch ; Geert Poels ; Frederik Gailly ; Monique Snoeck. Springer Science+Business Media, 2022. pp. 443-459 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13295 LNCS).

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title = "Enhancing Semantics-Driven Recommender Systems with Visual Features",

abstract = "Content-based semantics-driven recommender systems are often used in the small-scale news recommendation domain, founded on the TF-IDF measure but also taking into account domain semantics through semantic lexicons or ontologies. This work explores the application of content-based semantics-driven recommender systems to large-scale recommendations on the example of movie domain. We propose methods to extract semantic features from various item descriptions, including images. In particular, we use computer vision to extract semantic features from images and use these for recommendation together with various features extracted from textual information. The semantics-driven approach is scaled up with pre-computation of the cosine similarities and gradient learning of the model. The results of the study on a large-scale MovieLens dataset of user ratings demonstrate that semantics-driven recommenders can be extended to more complex domains and outperform TF-IDF on ROC, PR, F1, and Kappa metrics.",

author = "Bendouch, {Mounir M.} and Flavius Frasincar and Tarmo Robal",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 34th International Conference on Advanced Information Systems Engineering, CAiSE 2022 ; Conference date: 06-06-2022 Through 10-06-2022",

year = "2022",

doi = "10.1007/978-3-031-07472-1_26",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Bendouch, MM, Frasincar, F & Robal, T 2022, Enhancing Semantics-Driven Recommender Systems with Visual Features. in X Franch, G Poels, F Gailly & M Snoeck (eds), Advanced Information Systems Engineering (caise 2022). Springer Science+Business Media, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13295 LNCS, pp. 443-459, 34th International Conference on Advanced Information Systems Engineering, CAiSE 2022, Leuven, Belgium, 6/06/22. https://doi.org/10.1007/978-3-031-07472-1_26

Enhancing Semantics-Driven Recommender Systems with Visual Features. / Bendouch, Mounir M.; Frasincar, Flavius; Robal, Tarmo.
Advanced Information Systems Engineering (caise 2022). ed. / Xavier Franch; Geert Poels; Frederik Gailly; Monique Snoeck. Springer Science+Business Media, 2022. p. 443-459 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13295 LNCS).

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

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AU - Robal, Tarmo

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N2 - Content-based semantics-driven recommender systems are often used in the small-scale news recommendation domain, founded on the TF-IDF measure but also taking into account domain semantics through semantic lexicons or ontologies. This work explores the application of content-based semantics-driven recommender systems to large-scale recommendations on the example of movie domain. We propose methods to extract semantic features from various item descriptions, including images. In particular, we use computer vision to extract semantic features from images and use these for recommendation together with various features extracted from textual information. The semantics-driven approach is scaled up with pre-computation of the cosine similarities and gradient learning of the model. The results of the study on a large-scale MovieLens dataset of user ratings demonstrate that semantics-driven recommenders can be extended to more complex domains and outperform TF-IDF on ROC, PR, F1, and Kappa metrics.

AB - Content-based semantics-driven recommender systems are often used in the small-scale news recommendation domain, founded on the TF-IDF measure but also taking into account domain semantics through semantic lexicons or ontologies. This work explores the application of content-based semantics-driven recommender systems to large-scale recommendations on the example of movie domain. We propose methods to extract semantic features from various item descriptions, including images. In particular, we use computer vision to extract semantic features from images and use these for recommendation together with various features extracted from textual information. The semantics-driven approach is scaled up with pre-computation of the cosine similarities and gradient learning of the model. The results of the study on a large-scale MovieLens dataset of user ratings demonstrate that semantics-driven recommenders can be extended to more complex domains and outperform TF-IDF on ROC, PR, F1, and Kappa metrics.

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Bendouch MM, Frasincar F, Robal T. Enhancing Semantics-Driven Recommender Systems with Visual Features. In Franch X, Poels G, Gailly F, Snoeck M, editors, Advanced Information Systems Engineering (caise 2022). Springer Science+Business Media. 2022. p. 443-459. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13295 LNCS). doi: 10.1007/978-3-031-07472-1_26

Enhancing Semantics-Driven Recommender Systems with Visual Features

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