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An improved collaborative recommendation algorithm based on optimized user similarity

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Abstract

There are lots of issues existing in traditional collaborative filtering recommendation, such as data scarcities, cold start, recommendation accuracy and timeliness. And how to improve the efficiency and quality of recommendation is a key problem in collaborative recommendation. In the traditional collaborative filtering algorithms, the rating scale of different users for all projects sometimes may be neglected while calculating the similarity. Some algorithms such as adjusted cosine similarity algorithm and the Pearson similarity algorithm are proposed to optimize this problem, but there still exists the problem that the single rating scale is different for the same project with different users. It may result in similar resultant vector results when the users have significant differences for the score vectors on a common set. The substantial presence of this kind of phenomena has a direct impact on the accuracy of user similarity calculation. Furthermore, it will affect the target user’s predicted score accuracy. To solve the problem, an improved collaborative recommendation algorithm based on optimized user similarity is proposed. A balancing factor is added to the traditional cosine similarity algorithm, which is used to calculate the project rating scale differences between different users. Also, the most appropriate balance factor threshold can be obtained by experiments, a series of reasonable experiments to validate the effectiveness of the proposed algorithm based on the threshold. Experimental results show that the proposed improved collaborative filtering algorithm based on user similarity can significantly optimize the accuracy of user similarity and get better recommendation results.

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References

  1. Kim H, El A (2011) Collaborative error-reflected models for cold-start recommender systems. Decis Support Syst 51(3):519–531

    Article  Google Scholar 

  2. Bobadilla J, Ortega F, Hernando A (2012) A collaborative filtering similarity measure based on singularities. Inf Process Manag 48(2):204–217

    Article  Google Scholar 

  3. Goldberg D et al (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70

  4. Konstan JA et al (1997) Grouplens: applying collaborative filtering to usenet news. Commun ACM

  5. Pirasteh P, Jung JJ, Hwang D (2014) Item-based collaborative filtering with attribute correlation: a case study on movie recommendation. In: Intelligent information and database systems. Springer International Publishing, pp 245–252

  6. Pitsilis G, Knapskog SJ (2012) Social trust as a solution to address sparsity-inherent problems of Recommender systems. 19:332–344. arXiv:1208.1004

  7. Kumar R, Verma BK, Rastogi SS (2014) Social popularity based SVD++ recommender system. In J Comput Appl 87:33–37

    Google Scholar 

  8. Tsai CF, Hung C (2012) Cluster ensembles in collaborative filtering recommendation. Appl Soft Comput 12(4):1417–1425

    Article  Google Scholar 

  9. Zhang J, Hu X, Zhang Y et al (2011) An efficient ensemble method for classifying skewed data streams. In: Bio-inspired computing and applications. Springer, Berlin, Heidelberg, pp 144–151

  10. Li B (2011) Cross-domain collaborative filtering: a brief survey. In: Proceedings of the 23rd international conference on tools with artificial intelligence (ICTAI) IEEE Press, pp 1085–1086

  11. Ning X, Karypis G (2010) Multi-task learning for recommender system. In: Proceedings of the 2nd Asian conference on machine learning, Tokyo, Japan [s. n.], pp 269–284

  12. Li B, Yang Q, Xue X (2009 ) Transfer learning for collaborative filtering via a rating-matrix generative model. In: Proceedings of the 26th annual international conference on machine learning. ACM, New York, pp 617–624

  13. Singh AP, Gordon GJ (2008) Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining [S. l.]. ACM Press, pp 650–658

  14. Pan W, Evan WX, Lin N et al (2010 ) Transfer learning in collaborative filtering for sparsity reduction. In: AAAI, vol. 10, pp 230–235

  15. Pan W, Evan WX, Yang Q (2012) Transfer learning in collaborative filtering with uncertain ratings. In: Proceedings of the 26th AAAI conference on artificial intelligence. AAAI Press, Toronto, Canada pp 662–668

  16. Lu L, Medo M, Yeung CH, Zhang YC, Zhang ZK, Zhou T (2012) Recommender systems. Phys Rep 519(1):1–49

    Article  Google Scholar 

  17. Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12(4):331–370

    Article  MATH  Google Scholar 

  18. Ungar L, Foster D (1998) Clustering methods for collaborative filtering. In: Recommender systems-papers from the AAAI workshop, Madison

  19. Konstan J, Miller BN, Maltz D, Herlocker JL, Gordon L, Riedl J (1997) Applying collaborative filtering to usenet news. Commun ACM 40(3):77–87

    Article  Google Scholar 

  20. Liu S, Cheng X, Lan C et al (2013) Fractal property of generalized M-set with rational number exponent. Appl Math Comput 220:668–675

    MathSciNet  MATH  Google Scholar 

  21. Hofmann T, Puzicha J (1999) Latent class models for collaborative filtering. In: Proceedings of the 17th international joint conference on artificial intelligence, pp 688–693

  22. Menaouer B, Atmani B, Matta N (2013) Dynamic knowledge mapping guided by data mining: application on healthcare. J Inf Process Syst 9(1):1–30

    Article  Google Scholar 

  23. Liu S, Fu W, He L et al (2015) Distribution of primary additional errors in fractal encoding method. Multimed Tools Appl. doi:10.1007/s11042-014-2408-1

  24. Masashi K (2014) Task context-aware e-mail platform for collaborative tasks. Hum-Cent Comput Inf Sci 4(1):1–10

    Article  Google Scholar 

  25. Ghimire Deepak (2014) Extreme learning machine ensemble using bagging for facial expression recognition. J Inf Process Syst 10(3):443–458

    Article  MathSciNet  Google Scholar 

  26. Liu S, Cheng X, Fu W et al (2014) Numeric characteristics of generalized M-set with its asymptote. Appl Math Comput 243:767–774

    MathSciNet  MATH  Google Scholar 

  27. Motavaselalhagh F, Safi Esfahani F, Arabnia HR (2015) Knowledge-based adaptable scheduler for SaaS providers in cloud computing. Hum-Cent Comput Inf Sci 5(1):1–19

  28. Eung-Jong L, Kim C-H, Jung ImY (2014) An intelligent green service in internet of things. J Converg 5(3):4–8

  29. Rajinder S, Singh P, Duhan M (2014) An effective implementation of security based algorithmic approach in mobile adhoc networks. Hum-Cent Comput Inf Sci 4(1):1–14

    Article  Google Scholar 

  30. Kun P (2013) A secure network for mobile wireless service. JIPS 9(2):247–258

    MathSciNet  Google Scholar 

  31. Haeryong C, Choi M (2014) Personal mobile album/diary application development. J Converg 5(1):32–37

  32. Panjai T, Leangsuksun C (2014) Accessibility improvement in content-centric network. J Converg Inf Technol 9(2):156–166

  33. Resnick P, Iacovou N, Suchak M, Bergstron P, Riedl J (1994) Grouplens. An open architecture for collaborative filtering of net news. In: Proceeding of the 1994 ACM conference on computer supported cooperative work. ACM, New York pp 175–186

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Acknowledgments

This paper is partly supported by the National Science Foundation of China (Grant Nos. 61472132, 61472131 and 61300218).

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Hao Chen, Zhongkun Li & Wei Hu

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  1. Hao Chen
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  2. Zhongkun Li
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  3. Wei Hu
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Correspondence to Hao Chen.

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Chen, H., Li, Z. & Hu, W. An improved collaborative recommendation algorithm based on optimized user similarity. J Supercomput 72, 2565–2578 (2016). https://doi.org/10.1007/s11227-015-1518-5

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  • DOI: https://doi.org/10.1007/s11227-015-1518-5

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