Username   Password       Forgot your password?  Forgot your username? 


ICFLSB: An Improved Collaborative Filtering Algorithm based on Latent Semantic and Bayesian

Volume 14, Number 1, January 2018, pp. 26-36
DOI: 10.23940/ijpe.18.01.p4.2636

Yun Wua, Ren Qiana, Xiaofei Dongb, Yiqiao Lia, and Xinwei Niuc

aCollege of Computer Science and Technology, Guizhou University, Guiyang, China
bSchool of Mechanical Engineering, Yancheng Institute of Technology, Yancheng, China
cSchool of Engineering, Penn State Behrend, Erie, Pennsylvania, United States

(Submitted on November 1, 2017; Revised on December 10, 2017; Accepted on December 20, 2017)


In the process of user-based collaborative filtering algorithm, finding similar users effectively plays a crucial role in obtaining a high recommendation accuracy. The original rating matrix is very sparse, resulting in similarity information loss during similarity calculating and degrading the efficiency of similar users extracting. To tackle this problem, we propose an improved collaborative filtering algorithm based on Latent Semantic and Bayesian (ICFLSB). ICFLSB first utilizes Latent Semantic to extract meaningful features in the original rating matrix. Then, we establish a Bayesian model based on these extracted features to predict items which users have not rated but may be interested. Further, we fill the sparse original rating matrix with these predicted items and find similar users. After that, we adopt the collaborative filtering algorithm to conduct recommendations. Experiments show that ICFLSB proposed in this paper has a better recommendation performance than the traditional collaborative filtering algorithm. In particular, the evaluation results demonstrate that our ICFLSB can achieve a 2.152% higher and 1.152% higher on recommendation accuracy and recall rate respectively when compared to the traditional collaborative filtering algorithm.


References: 21

1. F. Anishya and K. M. Suresh, "A Novel Approach for Travel Package Recommendation Using Bayesian Approach," International Conference on Computing and Communications Technologies. IEEE, pp.296-301, 2015
2. Q. Ba, X. Li and Z. Bai, "Clustering Collaborative Filtering Recommendation System Based on SVD Algorithm," IEEE International Conference on Software Engineering and Service Science. IEEE, pp. 963-967, 2013
3. D. Banas, C. Havrilova, and J. Paralic, "Combination of User Profile Information and Collaborative Filtering in Recommendations," International Conference on Intelligent Engineering Systems. IEEE, pp. 359-363, 2015
4. M. Ciesielczyk, A. Szwabe and M. Morzy, "Progressive Random Indexing: Dimensionality Reduction Preserving Local Network Dependencies," Acm Transactions on Internet Technology, vol. 17, no. 2, pp. 20, 2017
5. G. Guo, J. Zhang and N. Yorkesmith, "A Novel Evidence-Based Bayesian Similarity Measure for Recommender Systems," Acm Transactions on the Web, vol. 10, no. 2, pp. 1-30, 2016
6. L. Hui, H. Yun, and L. Cunhua, "Personalization Recommendation Algorithm Based on Nearest Neighbor Relation," Computer Engineering and Applications, vol. 48, no. 36, pp. 205-209, 2012
7. B. O. Ibrahim, N. Ithnin, and M. Nilashi, "A Multi-criteria Recommendation System Using Dimensionality Reduction and Neuro-Fuzzy Techniques,"  Soft Computing, vol. 19, no. 11, pp. 3173-3207, 2015
8. Y. Ji, W. Hong, and J. Qi, "Missing Value Prediction Using Co-clustering and RBF for Collaborative Filtering," International Conference on Cloud Computing and Big Data. IEEE, pp. 350-353,2015
9. C. Kaleli, "An Entropy-based Neighbor Selection Approach for Collaborative Filtering,"  Knowledge-Based Systems, vol. 56,pp. 273-280, 2014
10. X. Liu, "An Improved Clustering-based Collaborative Filtering Recommendation Algorithm,"  Cluster Computing, pp. 1-8, 2017
11. Z. Liu, "Collaborative Filtering Recommendation Algorithm Based on User Interests," International Journal of u- and e- Service, Science and Technology, vol. 8, 2015
12. J. Mao, Z. Cui, and P. Zhao, "An Improved Similarity Measure Method in Collaborative Filtering Recommendation Algorithm," International Conference on Cloud Computing and Big Data,  pp. 297-303, 2013
13. S. Peng, Z. B. Zhou, and G. J. Wang, "Collaborative Filtering Algorithm Based on Rating Matrix Pre-filling," Computer Engineering, vol. 39, no. 1, pp. 175-178,  2013
14. D. Shin, S. Cetintas, and K. C. Lee, "Tumblr Blog Recommendation with Boosted Inductive Matrix Completion,"  ACM International, ACM, pp.203-212, 2015
15. H. Wang, N. Wang, and D. Y. Yeung, "Collaborative Deep Learning for Recommender Systems," ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp. 1235-1244, 2015
16. Q. Wang, X. Liu, and S. Zhang, "A Novel APP Recommendation Method Based on SVD and Social Influence," Algorithms and Architectures for Parallel Processing. 2015
17. X. Y. Wang and H. Liu, "Collaborative Filtering Recommendation Algorithm Integrated into Co-Rating Impact Factor," Advanced Materials Research, pp. 926-930, 2014
18. H. Wen, G. Ding, and C. Liu, "Matrix Factorization Meets Cosine Similarity: Addressing Sparsity Problem in Collaborative Filtering Recommender System, "Web Technologies and Applications. Springer International Publishing, pp. 306-317, 2014
19. M. Yan, W. Shang and Z. Li, "Application of SVD Technology in Video Recommendation System," Ieee/acis, International Conference on Computer and Information Science. IEEE, pp.1-5, 2016
20. Q. Yuan, G. Cong, and K. Zhao, "Who, Where, When, and What: A Nonparametric Bayesian Approach to Context-aware Recommendation and Search for Twitter Users,"  Acm Transactions on Information Systems, vol. 33, no. 1, 2015
21. W. Y. Zhang, S. S. Guo and S. Zhang, "Combining Hyperlink-induced Topic Search and Bayesian Approach for Personalised Manufacturing Service Recommendation," Taylor and Francis Ltd, pp. 1152-1163, 2016


Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader


Download this file (IJPE-2018-01-04.pdf)IJPE-2018-01-04.pdf[ICFLSB: An Improved Collaborative Filtering Algorithm based on Latent Semantic and Bayesian]395 Kb
This site uses encryption for transmitting your passwords.