A unified latent factor model for effective category-aware recommendation

Zhu Sun; Guibing Guo; Jie Zhang; Chi Xu

doi:10.1145/3079628.3079649

A unified latent factor model for effective category-aware recommendation

Zhu Sun, Guibing Guo, Jie Zhang, Chi Xu

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

4 Citations (Scopus)

Abstract

Our data analysis on real-world datasets shows that user preferences are intimately related with item categories, implying the nonnegligible of category information for effective recommendation. Thus, in this paper, step by step we propose a unified item-category latent factor model by considering user-category, item-category and category-category interactions. Our approach can be applied to both the situations where an item belongs to either a single category (one-To-one) or multiple categories (one-To-many). Finally, empirical studies on the real-world datasets demonstrate the superiority of our approach in comparison with other counterparts.

Original language	English
Title of host publication	UMAP 2017
Subtitle of host publication	Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
Place of Publication	New York, NY
Publisher	Association for Computing Machinery (ACM)
Pages	389-390
Number of pages	2
ISBN (Electronic)	9781450346351
DOIs	https://doi.org/10.1145/3079628.3079649
Publication status	Published - 9 Jul 2017
Externally published	Yes
Event	25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017 - Bratislava, Slovakia Duration: 9 Jul 2017 → 12 Jul 2017

Conference

Conference	25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017
Country/Territory	Slovakia
City	Bratislava
Period	9/07/17 → 12/07/17

Access to Document

10.1145/3079628.3079649

Cite this

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title = "A unified latent factor model for effective category-aware recommendation",

abstract = "Our data analysis on real-world datasets shows that user preferences are intimately related with item categories, implying the nonnegligible of category information for effective recommendation. Thus, in this paper, step by step we propose a unified item-category latent factor model by considering user-category, item-category and category-category interactions. Our approach can be applied to both the situations where an item belongs to either a single category (one-To-one) or multiple categories (one-To-many). Finally, empirical studies on the real-world datasets demonstrate the superiority of our approach in comparison with other counterparts.",

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

year = "2017",

month = jul,

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doi = "10.1145/3079628.3079649",

language = "English",

pages = "389--390",

booktitle = "UMAP 2017",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017 ; Conference date: 09-07-2017 Through 12-07-2017",

}

Sun, Z, Guo, G, Zhang, J & Xu, C 2017, A unified latent factor model for effective category-aware recommendation. in UMAP 2017: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery (ACM), New York, NY, pp. 389-390, 25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017, Bratislava, Slovakia, 9/07/17. https://doi.org/10.1145/3079628.3079649

A unified latent factor model for effective category-aware recommendation. / Sun, Zhu; Guo, Guibing; Zhang, Jie et al.
UMAP 2017: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. New York, NY: Association for Computing Machinery (ACM), 2017. p. 389-390.

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

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AU - Xu, Chi

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AB - Our data analysis on real-world datasets shows that user preferences are intimately related with item categories, implying the nonnegligible of category information for effective recommendation. Thus, in this paper, step by step we propose a unified item-category latent factor model by considering user-category, item-category and category-category interactions. Our approach can be applied to both the situations where an item belongs to either a single category (one-To-one) or multiple categories (one-To-many). Finally, empirical studies on the real-world datasets demonstrate the superiority of our approach in comparison with other counterparts.

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A unified latent factor model for effective category-aware recommendation

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