Graph-based recommendations: Make the most out of social data

Amit Tiroshi; Shlomo Berkovsky; Mohamed Ali Kaafar; David Vallet; Tsvi Kuflik

doi:10.1007/978-3-319-08786-3_40

Graph-based recommendations: Make the most out of social data

Amit Tiroshi, Shlomo Berkovsky, Mohamed Ali Kaafar, David Vallet, Tsvi Kuflik

Department of Information Systems

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

Abstract

Recommender systems use nowadays more and more data about users and items as part of the recommendation process. The availability of auxiliary data, going beyond the mere user/item data, has the potential to improve recommendations. In this work we examine the contribution of two types of social auxiliary data – namely, tags and friendship links – to the accuracy of a graph-based recommender. We measure the impact of the availability of auxiliary data on the recommendations using features extracted from both the auxiliary and the original data. The evaluation shows that the social auxiliary data improves the accuracy of the recommendations, and that the greatest improvement is achieved when graph features mirroring the nature of the auxiliary data are extracted by the recommender.

Original language	English
Title of host publication	User Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings
Editors	Vania Dimitrova, Tsvi Kuflik, David Chin, Francesco Ricci, Peter Dolog, Geert-Jan Houben
Publisher	Springer Verlag
Pages	447-458
Number of pages	12
ISBN (Electronic)	9783319087856
DOIs	https://doi.org/10.1007/978-3-319-08786-3_40
State	Published - 2014
Event	22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 - Aalborg, Netherlands Duration: 7 Jul 2014 → 11 Jul 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8538
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014
Country/Territory	Netherlands
City	Aalborg
Period	7/07/14 → 11/07/14

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2014.

Keywords

Feature extraction
Graph-based recommendations
Music recommendations
Social data

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science (all)

Access to Document

10.1007/978-3-319-08786-3_40

Cite this

Tiroshi, A., Berkovsky, S., Kaafar, M. A., Vallet, D., & Kuflik, T. (2014). Graph-based recommendations: Make the most out of social data. 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, Proceedings (pp. 447-458). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8538). Springer Verlag. https://doi.org/10.1007/978-3-319-08786-3_40

Tiroshi, Amit ; Berkovsky, Shlomo ; Kaafar, Mohamed Ali et al. / Graph-based recommendations : Make the most out of social data. User Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings. editor / Vania Dimitrova ; Tsvi Kuflik ; David Chin ; Francesco Ricci ; Peter Dolog ; Geert-Jan Houben. Springer Verlag, 2014. pp. 447-458 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Recommender systems use nowadays more and more data about users and items as part of the recommendation process. The availability of auxiliary data, going beyond the mere user/item data, has the potential to improve recommendations. In this work we examine the contribution of two types of social auxiliary data – namely, tags and friendship links – to the accuracy of a graph-based recommender. We measure the impact of the availability of auxiliary data on the recommendations using features extracted from both the auxiliary and the original data. The evaluation shows that the social auxiliary data improves the accuracy of the recommendations, and that the greatest improvement is achieved when graph features mirroring the nature of the auxiliary data are extracted by the recommender.",

keywords = "Feature extraction, Graph-based recommendations, Music recommendations, Social data",

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Tiroshi, A, Berkovsky, S, Kaafar, MA, Vallet, D & Kuflik, T 2014, Graph-based recommendations: Make the most out of social data. 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, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8538, Springer Verlag, pp. 447-458, 22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014, Aalborg, Netherlands, 7/07/14. https://doi.org/10.1007/978-3-319-08786-3_40

Graph-based recommendations: Make the most out of social data. / Tiroshi, Amit; Berkovsky, Shlomo; Kaafar, Mohamed Ali et al.
User Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings. ed. / Vania Dimitrova; Tsvi Kuflik; David Chin; Francesco Ricci; Peter Dolog; Geert-Jan Houben. Springer Verlag, 2014. p. 447-458 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8538).

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

TY - GEN

T1 - Graph-based recommendations

T2 - 22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014

AU - Tiroshi, Amit

AU - Berkovsky, Shlomo

AU - Kaafar, Mohamed Ali

AU - Vallet, David

AU - Kuflik, Tsvi

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - Recommender systems use nowadays more and more data about users and items as part of the recommendation process. The availability of auxiliary data, going beyond the mere user/item data, has the potential to improve recommendations. In this work we examine the contribution of two types of social auxiliary data – namely, tags and friendship links – to the accuracy of a graph-based recommender. We measure the impact of the availability of auxiliary data on the recommendations using features extracted from both the auxiliary and the original data. The evaluation shows that the social auxiliary data improves the accuracy of the recommendations, and that the greatest improvement is achieved when graph features mirroring the nature of the auxiliary data are extracted by the recommender.

AB - Recommender systems use nowadays more and more data about users and items as part of the recommendation process. The availability of auxiliary data, going beyond the mere user/item data, has the potential to improve recommendations. In this work we examine the contribution of two types of social auxiliary data – namely, tags and friendship links – to the accuracy of a graph-based recommender. We measure the impact of the availability of auxiliary data on the recommendations using features extracted from both the auxiliary and the original data. The evaluation shows that the social auxiliary data improves the accuracy of the recommendations, and that the greatest improvement is achieved when graph features mirroring the nature of the auxiliary data are extracted by the recommender.

KW - Feature extraction

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AN - SCOPUS:84927536939

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - User Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings

A2 - Dimitrova, Vania

A2 - Kuflik, Tsvi

A2 - Chin, David

A2 - Ricci, Francesco

A2 - Dolog, Peter

A2 - Houben, Geert-Jan

PB - Springer Verlag

Y2 - 7 July 2014 through 11 July 2014

ER -

Tiroshi A, Berkovsky S, Kaafar MA, Vallet D, Kuflik T. Graph-based recommendations: Make the most out of social data. In Dimitrova V, Kuflik T, Chin D, Ricci F, Dolog P, Houben G-J, editors, User Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings. Springer Verlag. 2014. p. 447-458. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-08786-3_40

Graph-based recommendations: Make the most out of social data

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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