IEICE TRANSACTIONS on Information
Improving Recommendation via Inference of User Popularity Preference in Sparse Data Environment
Summary :
The Collaborative Filtering (CF) algorithms work fairly well in personalized recommendation except in sparse data environment. To deal with the sparsity problem, researchers either take into account auxiliary information extracted from additional data resources, or set the missing ratings with default values, e.g., video popularity. Nevertheless, the former often costs high and incurs difficulty in knowledge transference whereas the latter degrades the accuracy and coverage of recommendation results. To our best knowledge, few literatures take advantage of users' preference on video popularity to tackle this problem. In this paper, we intend to enhance the performance of recommendation algorithm via the inference of the users' popularity preferences (PPs), especially in a sparse data environment. We propose a scheme to aggregate users' PPs and a Collaborative Filtering based algorithm to make the inference of PP feasible and effective from a small number of watching records. We modify a k-Nearest-Neighbor recommendation algorithm and a Matrix Factorization algorithm via introducing the inferred PP. Experiments on a large-scale commercial dataset show that the modified algorithm outperforms the original CF algorithms on both the recommendation accuracy and coverage. The significance of improvement is significant especially with the data sparsity.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.4 pp.1088-1095
- Publication Date
- 2018/04/01
- Publicized
- 2018/01/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017DAP0024
- Type of Manuscript
- Special Section PAPER (Special Section on Data Engineering and Information Management)
- Category
Authors
Xiaoying TAN
Beijing Jiaotong University
Yuchun GUO
Beijing Jiaotong University
Yishuai CHEN
Beijing Jiaotong University
Wei ZHU
PPTV Inc.
Keyword
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Xiaoying TAN, Yuchun GUO, Yishuai CHEN, Wei ZHU, "Improving Recommendation via Inference of User Popularity Preference in Sparse Data Environment" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 4, pp. 1088-1095, April 2018, doi: 10.1587/transinf.2017DAP0024.
Abstract: The Collaborative Filtering (CF) algorithms work fairly well in personalized recommendation except in sparse data environment. To deal with the sparsity problem, researchers either take into account auxiliary information extracted from additional data resources, or set the missing ratings with default values, e.g., video popularity. Nevertheless, the former often costs high and incurs difficulty in knowledge transference whereas the latter degrades the accuracy and coverage of recommendation results. To our best knowledge, few literatures take advantage of users' preference on video popularity to tackle this problem. In this paper, we intend to enhance the performance of recommendation algorithm via the inference of the users' popularity preferences (PPs), especially in a sparse data environment. We propose a scheme to aggregate users' PPs and a Collaborative Filtering based algorithm to make the inference of PP feasible and effective from a small number of watching records. We modify a k-Nearest-Neighbor recommendation algorithm and a Matrix Factorization algorithm via introducing the inferred PP. Experiments on a large-scale commercial dataset show that the modified algorithm outperforms the original CF algorithms on both the recommendation accuracy and coverage. The significance of improvement is significant especially with the data sparsity.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017DAP0024/_p
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@ARTICLE{e101-d_4_1088,
author={Xiaoying TAN, Yuchun GUO, Yishuai CHEN, Wei ZHU, },
journal={IEICE TRANSACTIONS on Information},
title={Improving Recommendation via Inference of User Popularity Preference in Sparse Data Environment},
year={2018},
volume={E101-D},
number={4},
pages={1088-1095},
abstract={The Collaborative Filtering (CF) algorithms work fairly well in personalized recommendation except in sparse data environment. To deal with the sparsity problem, researchers either take into account auxiliary information extracted from additional data resources, or set the missing ratings with default values, e.g., video popularity. Nevertheless, the former often costs high and incurs difficulty in knowledge transference whereas the latter degrades the accuracy and coverage of recommendation results. To our best knowledge, few literatures take advantage of users' preference on video popularity to tackle this problem. In this paper, we intend to enhance the performance of recommendation algorithm via the inference of the users' popularity preferences (PPs), especially in a sparse data environment. We propose a scheme to aggregate users' PPs and a Collaborative Filtering based algorithm to make the inference of PP feasible and effective from a small number of watching records. We modify a k-Nearest-Neighbor recommendation algorithm and a Matrix Factorization algorithm via introducing the inferred PP. Experiments on a large-scale commercial dataset show that the modified algorithm outperforms the original CF algorithms on both the recommendation accuracy and coverage. The significance of improvement is significant especially with the data sparsity.},
keywords={},
doi={10.1587/transinf.2017DAP0024},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Improving Recommendation via Inference of User Popularity Preference in Sparse Data Environment
T2 - IEICE TRANSACTIONS on Information
SP - 1088
EP - 1095
AU - Xiaoying TAN
AU - Yuchun GUO
AU - Yishuai CHEN
AU - Wei ZHU
PY - 2018
DO - 10.1587/transinf.2017DAP0024
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2018
AB - The Collaborative Filtering (CF) algorithms work fairly well in personalized recommendation except in sparse data environment. To deal with the sparsity problem, researchers either take into account auxiliary information extracted from additional data resources, or set the missing ratings with default values, e.g., video popularity. Nevertheless, the former often costs high and incurs difficulty in knowledge transference whereas the latter degrades the accuracy and coverage of recommendation results. To our best knowledge, few literatures take advantage of users' preference on video popularity to tackle this problem. In this paper, we intend to enhance the performance of recommendation algorithm via the inference of the users' popularity preferences (PPs), especially in a sparse data environment. We propose a scheme to aggregate users' PPs and a Collaborative Filtering based algorithm to make the inference of PP feasible and effective from a small number of watching records. We modify a k-Nearest-Neighbor recommendation algorithm and a Matrix Factorization algorithm via introducing the inferred PP. Experiments on a large-scale commercial dataset show that the modified algorithm outperforms the original CF algorithms on both the recommendation accuracy and coverage. The significance of improvement is significant especially with the data sparsity.
ER -
English
