CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-10-15
Cited: 0
Clicked: 8954
Ming Yang, Ying-ming Li, Zhongfei (Mark) Zhang. Scientific articles recommendation with topic regression and relational matrix factorization[J]. Journal of Zhejiang University Science C, 2014, 15(11): 984-998.
@article{title="Scientific articles recommendation with topic regression and relational matrix factorization",
author="Ming Yang, Ying-ming Li, Zhongfei (Mark) Zhang",
journal="Journal of Zhejiang University Science C",
volume="15",
number="11",
pages="984-998",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300374"
}
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%T Scientific articles recommendation with topic regression and relational matrix factorization
%A Ming Yang
%A Ying-ming Li
%A Zhongfei (Mark) Zhang
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 11
%P 984-998
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%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300374
TY - JOUR
T1 - Scientific articles recommendation with topic regression and relational matrix factorization
A1 - Ming Yang
A1 - Ying-ming Li
A1 - Zhongfei (Mark) Zhang
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 11
SP - 984
EP - 998
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300374
Abstract: In this paper we study the problem of recommending scientific articles to users in an online community with a new perspective of considering topic regression modeling and articles relational structure analysis simultaneously. First, we present a novel topic regression model, the topic regression matrix factorization (tr-MF), to solve the problem. The main idea of tr-MF lies in extending the matrix factorization with a probabilistic topic modeling. In particular, tr-MF introduces a regression model to regularize user factors through probabilistic topic modeling under the basic hypothesis that users share similar preferences if they rate similar sets of items. Consequently, tr-MF provides interpretable latent factors for users and items, and makes accurate predictions for community users. To incorporate the relational structure into the framework of tr-MF, we introduce relational matrix factorization. Through combining tr-MF with the relational matrix factorization, we propose the topic regression collective matrix factorization (tr-CMF) model. In addition, we also present the collaborative topic regression model with relational matrix factorization (CTR-RMF) model, which combines the existing collaborative topic regression (CTR) model and relational matrix factorization (RMF). From this point of view, CTR-RMF can be considered as an appropriate baseline for tr-CMF. Further, we demonstrate the efficacy of the proposed models on a large subset of the data from CiteULike, a bibliography sharing service dataset. The proposed models outperform the state-of-the-art matrix factorization models with a significant margin. Specifically, the proposed models are effective in making predictions for users with only few ratings or even no ratings, and support tasks that are specific to a certain field, neither of which has been addressed in the existing literature.
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