IEICE TRANSACTIONS on Information
Matrix Factorization Based Recommendation Algorithm for Sharing Patent Resource
Summary :
As scientific and technological resources are experiencing information overload, it is quite expensive to find resources that users are interested in exactly. The personalized recommendation system is a good candidate to solve this problem, but data sparseness and the cold starting problem still prevent the application of the recommendation system. Sparse data affects the quality of the similarity measurement and consequently the quality of the recommender system. In this paper, we propose a matrix factorization recommendation algorithm based on similarity calculation(SCMF), which introduces potential similarity relationships to solve the problem of data sparseness. A penalty factor is adopted in the latent item similarity matrix calculation to capture more real relationships furthermore. We compared our approach with other 6 recommendation algorithms and conducted experiments on 5 public data sets. According to the experimental results, the recommendation precision can improve by 2% to 9% versus the traditional best algorithm. As for sparse data sets, the prediction accuracy can also improve by 0.17% to 18%. Besides, our approach was applied to patent resource exploitation provided by the wanfang patents retrieval system. Experimental results show that our method performs better than commonly used algorithms, especially under the cold starting condition.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.8 pp.1250-1257
- Publication Date
- 2021/08/01
- Publicized
- 2021/04/26
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020BDP0012
- Type of Manuscript
- Special Section PAPER (Special Section on Computational Intelligence and Big Data for Scientific and Technological Resources and Services)
- Category
Authors
Xueqing ZHANG
Northwest University
Xiaoxia LIU
Northwest University
Jun GUO
Northwest University
Wenlei BAI
Northwest University
Daguang GAN
Wanfang Data Co.
Keyword
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Xueqing ZHANG, Xiaoxia LIU, Jun GUO, Wenlei BAI, Daguang GAN, "Matrix Factorization Based Recommendation Algorithm for Sharing Patent Resource" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 8, pp. 1250-1257, August 2021, doi: 10.1587/transinf.2020BDP0012.
Abstract: As scientific and technological resources are experiencing information overload, it is quite expensive to find resources that users are interested in exactly. The personalized recommendation system is a good candidate to solve this problem, but data sparseness and the cold starting problem still prevent the application of the recommendation system. Sparse data affects the quality of the similarity measurement and consequently the quality of the recommender system. In this paper, we propose a matrix factorization recommendation algorithm based on similarity calculation(SCMF), which introduces potential similarity relationships to solve the problem of data sparseness. A penalty factor is adopted in the latent item similarity matrix calculation to capture more real relationships furthermore. We compared our approach with other 6 recommendation algorithms and conducted experiments on 5 public data sets. According to the experimental results, the recommendation precision can improve by 2% to 9% versus the traditional best algorithm. As for sparse data sets, the prediction accuracy can also improve by 0.17% to 18%. Besides, our approach was applied to patent resource exploitation provided by the wanfang patents retrieval system. Experimental results show that our method performs better than commonly used algorithms, especially under the cold starting condition.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020BDP0012/_p
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@ARTICLE{e104-d_8_1250,
author={Xueqing ZHANG, Xiaoxia LIU, Jun GUO, Wenlei BAI, Daguang GAN, },
journal={IEICE TRANSACTIONS on Information},
title={Matrix Factorization Based Recommendation Algorithm for Sharing Patent Resource},
year={2021},
volume={E104-D},
number={8},
pages={1250-1257},
abstract={As scientific and technological resources are experiencing information overload, it is quite expensive to find resources that users are interested in exactly. The personalized recommendation system is a good candidate to solve this problem, but data sparseness and the cold starting problem still prevent the application of the recommendation system. Sparse data affects the quality of the similarity measurement and consequently the quality of the recommender system. In this paper, we propose a matrix factorization recommendation algorithm based on similarity calculation(SCMF), which introduces potential similarity relationships to solve the problem of data sparseness. A penalty factor is adopted in the latent item similarity matrix calculation to capture more real relationships furthermore. We compared our approach with other 6 recommendation algorithms and conducted experiments on 5 public data sets. According to the experimental results, the recommendation precision can improve by 2% to 9% versus the traditional best algorithm. As for sparse data sets, the prediction accuracy can also improve by 0.17% to 18%. Besides, our approach was applied to patent resource exploitation provided by the wanfang patents retrieval system. Experimental results show that our method performs better than commonly used algorithms, especially under the cold starting condition.},
keywords={},
doi={10.1587/transinf.2020BDP0012},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Matrix Factorization Based Recommendation Algorithm for Sharing Patent Resource
T2 - IEICE TRANSACTIONS on Information
SP - 1250
EP - 1257
AU - Xueqing ZHANG
AU - Xiaoxia LIU
AU - Jun GUO
AU - Wenlei BAI
AU - Daguang GAN
PY - 2021
DO - 10.1587/transinf.2020BDP0012
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2021
AB - As scientific and technological resources are experiencing information overload, it is quite expensive to find resources that users are interested in exactly. The personalized recommendation system is a good candidate to solve this problem, but data sparseness and the cold starting problem still prevent the application of the recommendation system. Sparse data affects the quality of the similarity measurement and consequently the quality of the recommender system. In this paper, we propose a matrix factorization recommendation algorithm based on similarity calculation(SCMF), which introduces potential similarity relationships to solve the problem of data sparseness. A penalty factor is adopted in the latent item similarity matrix calculation to capture more real relationships furthermore. We compared our approach with other 6 recommendation algorithms and conducted experiments on 5 public data sets. According to the experimental results, the recommendation precision can improve by 2% to 9% versus the traditional best algorithm. As for sparse data sets, the prediction accuracy can also improve by 0.17% to 18%. Besides, our approach was applied to patent resource exploitation provided by the wanfang patents retrieval system. Experimental results show that our method performs better than commonly used algorithms, especially under the cold starting condition.
ER -
English
