Open Access
| Issue |
Wuhan Univ. J. Nat. Sci.
Volume 28, Number 5, October 2023
|
|
|---|---|---|
| Page(s) | 451 - 460 | |
| DOI | https://doi.org/10.1051/wujns/2023285451 | |
| Published online | 10 November 2023 | |
- Chen J F, Yuan Y, Ruan T, et al. Hyper-parameter-evolutionary latent factor analysis for high-dimensional and sparse data from recommender systems[J]. Neurocomputing, 2021, 421: 316-328. [CrossRef] [Google Scholar]
- Yan J, Zeng Q T, Zhang F Q. Summary of recommendation algorithm research[J]. Journal of Physics: Conference Series, 2021, 1754(1): 012224. [NASA ADS] [CrossRef] [Google Scholar]
- Chen Y C, Hui L, Thaipisutikul T. A collaborative filtering recommendation system with dynamic time decay[J]. The Journal of Supercomputing, 2021, 77(1): 244-262. [CrossRef] [Google Scholar]
- Xue F, He X N, Wang X, et al. Deep item-based collaborative filtering for top-N recommendation[J]. ACM Transactions on Information Systems, 2019, 37(3): 1-25. [Google Scholar]
- Wu Y T, Zhang X M, Yu H, et al. Collaborative filtering recommendation algorithm based on user fuzzy similarity[J]. Intelligent Data Analysis, 2017, 21(2): 311-327. [CrossRef] [Google Scholar]
- George G, Lal A M. Hy-MOM: Hybrid recommender system framework using memory-based and model-based collaborative filtering framework[J]. Cybernetics and Information Technologies, 2022, 22(1): 134-150. [Google Scholar]
- Jia R, Li R, Gao M. Study on data sparsity in social network-based recommender system[J]. International Journal of Computational Science and Engineering, 2019, 20(1): 15. [CrossRef] [Google Scholar]
- Liu C H, Han C F, Chen T C, et al. Collaborative filtering recommendation algorithm based on penalty factors and time weights[J]. Cyber Security and Data Governance, 2020, 39(5): 17-21(Ch). [Google Scholar]
- Tao W C, Dang Y G. Collaborative filtering recommendation algorithm based on grey incidence clustering[J]. Operations Research and Management Science, 2018, 27(1): 84-88 (Ch). [Google Scholar]
- Wang Y, Deng J, Gao J, et al. A hybrid user similarity model for collaborative filtering[J]. Information Sciences, 2017, 418: 102-118. [CrossRef] [Google Scholar]
- Zhang R L, Zhang R, Wu X N, et al. Collaborative filtering recommendation algorithm based on mixed similarity and differential privacy[J]. Application Research of Computers, 2021, 38(8): 2334-2339(Ch). [Google Scholar]
- Chen Y, Liu Z Q. Research on improved recommendation algorithm based on LFM matrix factorization[J]. Computer Engineering and Applications, 2019, 55(2):116-120(Ch). [Google Scholar]
- Wang W J, Lu Y M. Analysis of the mean absolute error (MAE) and the root mean square error (RMSE) in assessing rounding model[J]. IOP Conference Series: Materials Science and Engineering, 2018, 324: 012049. [NASA ADS] [CrossRef] [Google Scholar]
- Xiang L. Practical Combat of Recommendation System[M]. Beijing: People's Post and Telecommunications Press, 2012(Ch). [Google Scholar]
- Anand R, Beel J. Auto-surprise: An automated recommender-system (AutoRecSys) library with tree of parzens estimator (TPE) optimization[C]//Fourteenth ACM Conference on Recommender Systems. New York: ACM, 2020: 585-587. [Google Scholar]
- Spark Apache. Spark mllib programming guide[EB/OL]. [2022-10-23]. https://spark.apache.org/mllib. [Google Scholar]
- MovieLens GroupLens. MovieLens data guide[EB/OL]. [2022-11-03]. https://grouplens.org/datasets/movielens. [Google Scholar]
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