CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-05-14
Cited: 0
Clicked: 6784
Bin Ju, Yun-tao Qian, Min-chao Ye. Preference transfer model in collaborative filtering for implicit data[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(6): 489-500.
@article{title="Preference transfer model in collaborative filtering for implicit data",
author="Bin Ju, Yun-tao Qian, Min-chao Ye",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="6",
pages="489-500",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500313"
}
%0 Journal Article
%T Preference transfer model in collaborative filtering for implicit data
%A Bin Ju
%A Yun-tao Qian
%A Min-chao Ye
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 6
%P 489-500
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500313
TY - JOUR
T1 - Preference transfer model in collaborative filtering for implicit data
A1 - Bin Ju
A1 - Yun-tao Qian
A1 - Min-chao Ye
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 6
SP - 489
EP - 500
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500313
Abstract: Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users’ buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized. Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then, two factor-user matrices can be used to construct a so-called ‘preference dictionary’ that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.
The authors propose to transfer data from browsing history of users into user-item matrix including bought items in order to improve the prediction accuracy of collaborative filtering schemes. Such filtering systems predict whether a user will buy an item or not. The authors also perform real data-based experiments to evaluate their proposed scheme. The paper is very clear and very well written. The paper focuses on an interesting problem.
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