Probabilistic Matrix Factorization Recommendation Model Incorporating Multiple Weighting Factors

WANG Dan; TIAN Guangqiang; WANG Fuzhong

doi:10.11999/JEIT210011

Volume 44 Issue 2

Feb. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(2): 552-565

WANG Dan, TIAN Guangqiang, WANG Fuzhong. Probabilistic Matrix Factorization Recommendation Model Incorporating Multiple Weighting Factors[J]. Journal of Electronics & Information Technology, 2022, 44(2): 552-565. doi: 10.11999/JEIT210011

Citation:

WANG Dan, TIAN Guangqiang, WANG Fuzhong. Probabilistic Matrix Factorization Recommendation Model Incorporating Multiple Weighting Factors[J]. Journal of Electronics & Information Technology, 2022, 44(2): 552-565. doi: 10.11999/JEIT210011

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Probabilistic Matrix Factorization Recommendation Model Incorporating Multiple Weighting Factors

doi: 10.11999/JEIT210011

1.
School of Intelligent Engineering, Huanghe Jiaotong University, Jiaozuo 454950, China
2.
School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China

Funds: Sub-projects of National Major Special Projects(22016YFC0600906), Training plan of young backbone teachers in Colleges and universities of Henan Province in 2019(2019286), The 13th five year plan of Educational Science in Henan Province(2020YB0404), Scientific research project of Jiaozuo Engineering Technology Center(201834), Key Subject Project of Computer Science and Technology in Huanghe Jiaotong University(201902)

Received Date: 2021-01-05
Rev Recd Date: 2021-06-30

Available Online: 2021-07-09

Publish Date: 2022-02-25

Abstract

Abstract

Considering the problems of low accuracy of personalized recommendation and sensitivity to cold start, low-rank Probabilistic Matrix Factorization recommendation model incorporating Multiple Weighting Factors (MWFPMF) is proposed; The trust network is constructed using a given social network, and the trust between users is calculated using the Page rank algorithm and trust transfer mechanism; The user’s social status is calculated based on Page rank, and the weight of the relationship between users is modified using activity scores and scoring time; Term Frequency-Inverse Document Frequency(TF-IDF) is introduced to take user tags, and the homogeneity between users is characterized by tag similarity; The three factors of trust among users, influence of users’ social status, and user homogeneity are integrated into the low-rank probability matrix decomposition, so that user preferences and activity characteristics are mapped to the same low-rank space, and the user-activity scoring matrix is decomposed. Under the premise of regularization as a constraint, the effective prediction of the lack of user ratings by the low-rank feature matrix is finally completed. The data sets of Douban Beijing and Ciao are used to determine the parameter settings of each module. Through simulation and comparison experiments, it can be seen that this recommendation model obtains higher recommendation model accuracy. Compared with the other five traditional recommendation algorithms, the mean absolute error is reduced by at least 6.58%, and the mean square error is reduced by at least 6.27%, compared with the deep learning advancing algorithm, the recommendation accuracy is almost the same; It has obvious advantages in cold-start user recommendation. Compared with other recommendation algorithms, the average absolute error is reduced by at least 0.89%, and the mean square error is reduced by at least 3.01%.
- Recommendation algorithm,
- Probabilistic matrix factorization,
- User trust,
- Social status influence,
- Homogeneity,
- Regularization constraints

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References(25)

References

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