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, doi: 10.3934/dcdss.2019054
References:
[1]

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

[2]

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

[3]

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

[4]

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

[5]

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

[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.

[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.

[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.

[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.

[10]

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

[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.

[12]

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

[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.

[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.

[15]

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

[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.

[17]

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

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

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.

[2]

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

[3]

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

[4]

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

[5]

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

[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.

[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.

[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.

[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.

[10]

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

[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.

[12]

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

[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.

[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.

[15]

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

[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.

[17]

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

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

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
[1]

Baolan Yuan, Wanjun Zhang, Yubo Yuan. A Max-Min clustering method for $k$-means algorithm of data clustering. Journal of Industrial & Management Optimization, 2012, 8 (3) : 565-575. doi: 10.3934/jimo.2012.8.565

[2]

Baoli Shi, Zhi-Feng Pang, Jing Xu. Image segmentation based on the hybrid total variation model and the K-means clustering strategy. Inverse Problems & Imaging, 2016, 10 (3) : 807-828. doi: 10.3934/ipi.2016022

[3]

Sung Ha Kang, Berta Sandberg, Andy M. Yip. A regularized k-means and multiphase scale segmentation. Inverse Problems & Imaging, 2011, 5 (2) : 407-429. doi: 10.3934/ipi.2011.5.407

[4]

Ruiqi Yang, Dachuan Xu, Yicheng Xu, Dongmei Zhang. An adaptive probabilistic algorithm for online k-center clustering. Journal of Industrial & Management Optimization, 2018, 13 (5) : 1-12. doi: 10.3934/jimo.2018057

[5]

Nicolás M. Crisosto, Christopher M. Kribs-Zaleta, Carlos Castillo-Chávez, Stephen Wirkus. Community resilience in collaborative learning. Discrete & Continuous Dynamical Systems - B, 2010, 14 (1) : 17-40. doi: 10.3934/dcdsb.2010.14.17

[6]

Gurkan Ozturk, Mehmet Tahir Ciftci. Clustering based polyhedral conic functions algorithm in classification. Journal of Industrial & Management Optimization, 2015, 11 (3) : 921-932. doi: 10.3934/jimo.2015.11.921

[7]

Weiping Li, Haiyan Wu, Jie Yang. Intelligent recognition algorithm for social network sensitive information based on classification technology. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 1385-1398. doi: 10.3934/dcdss.2019095

[8]

Guojun Gan, Kun Chen. A soft subspace clustering algorithm with log-transformed distances. Big Data & Information Analytics, 2016, 1 (1) : 93-109. doi: 10.3934/bdia.2016.1.93

[9]

Jiangchuan Fan, Xinyu Guo, Jianjun Du, Weiliang Wen, Xianju Lu, Brahmani Louiza. Analysis of the clustering fusion algorithm for multi-band color image. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 1233-1249. doi: 10.3934/dcdss.2019085

[10]

Sangkyu Baek, Jinsoo Park, Bong Dae Choi. Performance analysis of transmission rate control algorithm from readers to a middleware in intelligent transportation systems. Numerical Algebra, Control & Optimization, 2012, 2 (2) : 357-375. doi: 10.3934/naco.2012.2.357

[11]

Yang Chen, Xiaoguang Xu, Yong Wang. Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 887-900. doi: 10.3934/dcdss.2019059

[12]

Gaidi Li, Zhen Wang, Dachuan Xu. An approximation algorithm for the $k$-level facility location problem with submodular penalties. Journal of Industrial & Management Optimization, 2012, 8 (3) : 521-529. doi: 10.3934/jimo.2012.8.521

[13]

Haixia Liu, Jian-Feng Cai, Yang Wang. Subspace clustering by (k,k)-sparse matrix factorization. Inverse Problems & Imaging, 2017, 11 (3) : 539-551. doi: 10.3934/ipi.2017025

[14]

Guojun Gan, Qiujun Lan, Shiyang Sima. Scalable clustering by truncated fuzzy $c$-means. Big Data & Information Analytics, 2016, 1 (2&3) : 247-259. doi: 10.3934/bdia.2016007

[15]

Elnaz Delpisheh, Aijun An, Heidar Davoudi, Emad Gohari Boroujerdi. Time aware topic based recommender system. Big Data & Information Analytics, 2016, 1 (2&3) : 261-274. doi: 10.3934/bdia.2016008

[16]

M. Predescu, R. Levins, T. Awerbuch-Friedlander. Analysis of a nonlinear system for community intervention in mosquito control. Discrete & Continuous Dynamical Systems - B, 2006, 6 (3) : 605-622. doi: 10.3934/dcdsb.2006.6.605

[17]

Ayla Sayli, Ayse Oncu Sarihan. Statistical query-based rule derivation system by backward elimination algorithm. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1341-1356. doi: 10.3934/dcdss.2015.8.1341

[18]

Fabrice Delbary, Kim Knudsen. Numerical nonlinear complex geometrical optics algorithm for the 3D Calderón problem. Inverse Problems & Imaging, 2014, 8 (4) : 991-1012. doi: 10.3934/ipi.2014.8.991

[19]

Xiaojun Zhou, Chunhua Yang, Weihua Gui. State transition algorithm. Journal of Industrial & Management Optimization, 2012, 8 (4) : 1039-1056. doi: 10.3934/jimo.2012.8.1039

[20]

Eliana Pepa Risma. A deferred acceptance algorithm with contracts. Journal of Dynamics & Games, 2015, 2 (3&4) : 289-302. doi: 10.3934/jdg.2015005

2017 Impact Factor: 0.561

Metrics

  • PDF downloads (9)
  • HTML views (71)
  • Cited by (0)

Other articles
by authors

[Back to Top]