August & September  2019, 12(4&5): 811-822. doi: 10.3934/dcdss.2019054

Collaborative filtering recommendation algorithm towards intelligent community

Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

* Corresponding author: Wei Fu

Received  August 2017 Revised  January 2018 Published  November 2018

Collaborative filtering recommendation algorithm is a successful and widely used recommendation method in recommender system. In the collaborative filtering recommendation algorithm, the key step is to find the nearest neighbor. Combined with the application scenario of the intelligent community, Pearson Correlation Coefficient is introduced to improve the accuracy of similarity calculation. At the same time, considering that the residents are relatively fixed, the K-means clustering algorithm can be combined with the user-based collaborative filtering recommendation algorithm to improve the sparsity of the matrix and improve the speed of recommendation. Validation results on MovieLens dataset show that the collaborative filtering recommendation algorithm integrating with K-means clustering algorithm and community factors can more effectively predict the actual user rating in the community application scenario, and improve the recommendation accuracy and recommendation speed, compared with the traditional collaborative filtering recommendation algorithm.

Citation: Wei Fu, Jun Liu, Yirong Lai. Collaborative filtering recommendation algorithm towards intelligent community. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 811-822. doi: 10.3934/dcdss.2019054
References:
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L. Cui, L. Dong and X. Fu, et al, A Video Recommendation Algorithm Based on the Combination Of Video Content and Social Network, Concurrency & Computation Practice & Experience, 2016. Google Scholar

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G. M. Dakhel and M. Mahdavi, A new collaborative filtering algorithm using k-means clustering and neighbors’ voting, International Conference on Hybrid Intelligent Systems. IEEE, 2012, 179–184. Google Scholar

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J. L. Herlocker, J. A. Konstan and L. G. Terveen, et al, Evaluating collaborative filtering recommender systems, Acm Transactions on Information Systems, 22 (2004), 5–53. Google Scholar

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R. Katarya and O. P. Verma, An Effective Collaborative Movie Recommender System with Cuckoo Search, Egyptian Informatics Journal, 2016. Google Scholar

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M. Khoshneshin and W. N. Street, Incremental collaborative filtering via evolutionary coclustering, ACM Conference on Recommender Systems, Recsys 2010, Barcelona, Spain, September. DBLP, 2010,325–328. Google Scholar

[14]

Q. Lin, T. B. Zhang and Y. G. Wang, Framework Based on web Services for Intelligent Community Information System Software Integration, Computer Engineering & Design, 2004. Google Scholar

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G. LindenB. Smith and J. York, Amazon.com recommendations: Item-to-item collaborative filtering, IEEE Internet Computing, 7 (2003), 76-80.   Google Scholar

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Q. Liu, E. Chen and H. Xiong, et al, Enhancing collaborative filtering by user interest expansion via personalized ranking, IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 42 (2012), 218–233. Google Scholar

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J. Llibre, Centers: their integrability and relations with the divergence, Applied Mathematics and Nonlinear Sciences, 1 (2016), 79-86.   Google Scholar

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X. Mu, Y. Chen and T. Li, User-based collaborative filtering based on improved similarity algorithm, IEEE International Conference on Computer Science and Information Technology, IEEE, 2010, 76–80. Google Scholar

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B. Sarwar, G. Karypis and J. Konstan, et al, Item-based collaborative filtering recommendation algorithms, International Conference on World Wide Web. ACM, 2001, 285–295. Google Scholar

[20]

X. Y. Shi, H. W. Ye and S. J. Gong, A Personalized Recommender Integrating Item-Based and User-Based Collaborative Filtering, International Seminar on Business and Information Management Volume, 2008, 264–267. Google Scholar

[21]

J. Wan, Collaborative Filtering Recommendation Algorithm Based on User's Comprehensive Information Particle Swarm Optimization and K-means Clustering, Science and Engineering Research Center.Proceedings of 2015 International Conference on Industrial Informatics, Machinery and Materials(IIMM 2015)[C].Science and Engineering Research Center:, 2015: 7. Google Scholar

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U. WanaskarS. Vij and D. Mukhopadhyay, A hybrid web recommendation system based on the improved association rule mining algorithm, Journal of Software Engineering & Applications, 6 (2014), 32-36.   Google Scholar

[23]

Z. Wei, L. Nan and H. Ying, et al, User-based Collaborative Filtering Recommendation Algorithm Based on Improved K-Means Clustering, ournal of Anhui University, 2016. Google Scholar

[24]

Y. U. Xue and M. Q. Li, Collaborative filtering recommendation model based on effective dimension reduction and K-means clustering, Application Research of Computers, 26 (2009), 3718-3721.   Google Scholar

[25]

J. M. Yang and S. Liu, et al, An Evaluation of the Statistical Methods for Testing the Performance of Crop Models with Observed Data, Application Research of Computers, 2014. Google Scholar

[26]

D. H. Zhai, Y. U. Jiang and F. Gao, et al, K-means text clustering algorithm based on initial cluster centers selection according to maximum distance, Agricultural Systems, 127 (2014), 81–89. Google Scholar

[27]

L. ZhuY. Pan and J. T. Wang, Affine transformation based ontology sparse vector learning algorithm, Applied Mathematics and Nonlinear Sciences, 2 (2017), 111-122.   Google Scholar

show all references

References:
[1]

M. A. Andrew and B. Erik, Big data: The management revolution, Harvard Business Review, 90 (2012), 60-6, 68,128. Google Scholar

[2]

L. Anidorifón, J. Santosgago and M. Caeirorodr´ıguez, et al, Recommender systems, Communications of the Acm, 40 (2015), 56–58. Google Scholar

[3]

B. BasavanagoudV. R. Desai and S. Patil, $(β, α)$- connectivity index of graphs, Applied Mathematics and Nonlinear Sciences, 2 (2017), 21-30.   Google Scholar

[4]

H. Bian, K. F. Bai and H. Q. Zhao, et al, Study on Intelligent Community Construction Scheme, Shaanxi Electric Power, 2011. Google Scholar

[5]

J. BobadillaF. Ortega and A. Hernando, Recommender systems survey, Knowledge-Based Systems, 46 (2013), 109-132.   Google Scholar

[6]

W. Cheng and X. Yancai, et al, Analysis of intelligent community business model and operation mode, Power System Protection & Control, 43 (2015), 147–154. Google Scholar

[7]

L. Cui, L. Dong and X. Fu, et al, A Video Recommendation Algorithm Based on the Combination Of Video Content and Social Network, Concurrency & Computation Practice & Experience, 2016. Google Scholar

[8]

G. M. Dakhel and M. Mahdavi, A new collaborative filtering algorithm using k-means clustering and neighbors’ voting, International Conference on Hybrid Intelligent Systems. IEEE, 2012, 179–184. Google Scholar

[9]

X. Fei and Y. Gu, Progress in modifications and applications of fluorescent dye probe, Progress in Natural Science: Materials International, 19 (2009), 501-509.  doi: 10.1016/j.pnsc.2008.06.022.  Google Scholar

[10]

X. Y. He and Y. Zhang, Research on the Problems and Countermeasures of Intelligent Community Construction in China, Construction Economy, 2016. Google Scholar

[11]

J. L. Herlocker, J. A. Konstan and L. G. Terveen, et al, Evaluating collaborative filtering recommender systems, Acm Transactions on Information Systems, 22 (2004), 5–53. Google Scholar

[12]

R. Katarya and O. P. Verma, An Effective Collaborative Movie Recommender System with Cuckoo Search, Egyptian Informatics Journal, 2016. Google Scholar

[13]

M. Khoshneshin and W. N. Street, Incremental collaborative filtering via evolutionary coclustering, ACM Conference on Recommender Systems, Recsys 2010, Barcelona, Spain, September. DBLP, 2010,325–328. Google Scholar

[14]

Q. Lin, T. B. Zhang and Y. G. Wang, Framework Based on web Services for Intelligent Community Information System Software Integration, Computer Engineering & Design, 2004. Google Scholar

[15]

G. LindenB. Smith and J. York, Amazon.com recommendations: Item-to-item collaborative filtering, IEEE Internet Computing, 7 (2003), 76-80.   Google Scholar

[16]

Q. Liu, E. Chen and H. Xiong, et al, Enhancing collaborative filtering by user interest expansion via personalized ranking, IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 42 (2012), 218–233. Google Scholar

[17]

J. Llibre, Centers: their integrability and relations with the divergence, Applied Mathematics and Nonlinear Sciences, 1 (2016), 79-86.   Google Scholar

[18]

X. Mu, Y. Chen and T. Li, User-based collaborative filtering based on improved similarity algorithm, IEEE International Conference on Computer Science and Information Technology, IEEE, 2010, 76–80. Google Scholar

[19]

B. Sarwar, G. Karypis and J. Konstan, et al, Item-based collaborative filtering recommendation algorithms, International Conference on World Wide Web. ACM, 2001, 285–295. Google Scholar

[20]

X. Y. Shi, H. W. Ye and S. J. Gong, A Personalized Recommender Integrating Item-Based and User-Based Collaborative Filtering, International Seminar on Business and Information Management Volume, 2008, 264–267. Google Scholar

[21]

J. Wan, Collaborative Filtering Recommendation Algorithm Based on User's Comprehensive Information Particle Swarm Optimization and K-means Clustering, Science and Engineering Research Center.Proceedings of 2015 International Conference on Industrial Informatics, Machinery and Materials(IIMM 2015)[C].Science and Engineering Research Center:, 2015: 7. Google Scholar

[22]

U. WanaskarS. Vij and D. Mukhopadhyay, A hybrid web recommendation system based on the improved association rule mining algorithm, Journal of Software Engineering & Applications, 6 (2014), 32-36.   Google Scholar

[23]

Z. Wei, L. Nan and H. Ying, et al, User-based Collaborative Filtering Recommendation Algorithm Based on Improved K-Means Clustering, ournal of Anhui University, 2016. Google Scholar

[24]

Y. U. Xue and M. Q. Li, Collaborative filtering recommendation model based on effective dimension reduction and K-means clustering, Application Research of Computers, 26 (2009), 3718-3721.   Google Scholar

[25]

J. M. Yang and S. Liu, et al, An Evaluation of the Statistical Methods for Testing the Performance of Crop Models with Observed Data, Application Research of Computers, 2014. Google Scholar

[26]

D. H. Zhai, Y. U. Jiang and F. Gao, et al, K-means text clustering algorithm based on initial cluster centers selection according to maximum distance, Agricultural Systems, 127 (2014), 81–89. Google Scholar

[27]

L. ZhuY. Pan and J. T. Wang, Affine transformation based ontology sparse vector learning algorithm, Applied Mathematics and Nonlinear Sciences, 2 (2017), 111-122.   Google Scholar

Figure 1.  The design process of the algorithm
Figure 2.  MAE value of the recommendation algorithm
Figure 3.  Precision of recommendation algorithm
Figure 4.  Running time of recommendation algorithm
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