Effective recommendation with category hierarchy

Zhu Sun; Guibing Guo; Jie Zhang

doi:10.1145/2930238.2930269

Effective recommendation with category hierarchy

Zhu Sun, Guibing Guo, Jie Zhang

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

2 Citations (Scopus)

Abstract

Although at item category structure where categories are independent in a same level has been well studied to enhance recommendation performance, in many real applications, item category is often organized in hierarchies to reect the inherent correlations among categories. In this paper, we propose a novel matrix factorization model by exploiting category hierarchy from the perspectives of users and items for effective recommendation. Specifically, a user (an item) can be inuenced (characterized) by her preferred categories (the categories it belongs to) in the hierarchy. We incorporate how different categories in the hierarchy coinuence a user and an item. Empirical results show the superiority of our approach against other counterparts.

Original language	English
Title of host publication	Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization
Place of Publication	New York
Publisher	Association for Computing Machinery, Inc
Pages	299-300
Number of pages	2
ISBN (Electronic)	9781450343701
DOIs	https://doi.org/10.1145/2930238.2930269
Publication status	Published - 2016
Externally published	Yes
Event	24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016 - Halifax, Canada Duration: 13 Jul 2016 → 17 Jul 2016

Conference

Conference	24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016
Country/Territory	Canada
City	Halifax
Period	13/07/16 → 17/07/16

Keywords

Category hierarchy
Matrix factorization
Recommendation

Access to Document

10.1145/2930238.2930269

Cite this

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title = "Effective recommendation with category hierarchy",

abstract = "Although at item category structure where categories are independent in a same level has been well studied to enhance recommendation performance, in many real applications, item category is often organized in hierarchies to reect the inherent correlations among categories. In this paper, we propose a novel matrix factorization model by exploiting category hierarchy from the perspectives of users and items for effective recommendation. Specifically, a user (an item) can be inuenced (characterized) by her preferred categories (the categories it belongs to) in the hierarchy. We incorporate how different categories in the hierarchy coinuence a user and an item. Empirical results show the superiority of our approach against other counterparts.",

keywords = "Category hierarchy, Matrix factorization, Recommendation",

author = "Zhu Sun and Guibing Guo and Jie Zhang",

year = "2016",

doi = "10.1145/2930238.2930269",

language = "English",

pages = "299--300",

booktitle = "Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization",

publisher = "Association for Computing Machinery, Inc",

note = "24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016 ; Conference date: 13-07-2016 Through 17-07-2016",

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Sun, Z, Guo, G & Zhang, J 2016, Effective recommendation with category hierarchy. in Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization. Association for Computing Machinery, Inc, New York, pp. 299-300, 24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016, Halifax, Canada, 13/07/16. https://doi.org/10.1145/2930238.2930269

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T1 - Effective recommendation with category hierarchy

AU - Sun, Zhu

AU - Guo, Guibing

AU - Zhang, Jie

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AB - Although at item category structure where categories are independent in a same level has been well studied to enhance recommendation performance, in many real applications, item category is often organized in hierarchies to reect the inherent correlations among categories. In this paper, we propose a novel matrix factorization model by exploiting category hierarchy from the perspectives of users and items for effective recommendation. Specifically, a user (an item) can be inuenced (characterized) by her preferred categories (the categories it belongs to) in the hierarchy. We incorporate how different categories in the hierarchy coinuence a user and an item. Empirical results show the superiority of our approach against other counterparts.

KW - Category hierarchy

KW - Matrix factorization

KW - Recommendation

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DO - 10.1145/2930238.2930269

M3 - Conference abstract

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BT - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

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T2 - 24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016

Y2 - 13 July 2016 through 17 July 2016

ER -

Effective recommendation with category hierarchy

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