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Open Access

An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor

School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China.
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Abstract

The Collaborative Filtering (CF) recommendation algorithm, one of the most popular algorithms in Recommendation Systems (RS), mainly includes memory-based and model-based methods. When performing rating prediction using a memory-based method, the approach used to measure the similarity between users or items can significantly influence the recommendation performance. Traditional CFs suffer from data sparsity when making recommendations based on a rating matrix, and cannot effectively capture changes in user interest. In this paper, we propose an improved hybrid collaborative filtering algorithm based on tags and a time factor (TT-HybridCF), which fully utilizes tag information that characterizes users and items. This algorithm utilizes both tag and rating information to calculate the similarity between users or items. In addition, we introduce a time weighting factor to measure user interest, which changes over time. Our experimental results show that our method alleviates the sparsity problem and demonstrates promising prediction accuracy.

Keywords

recommendation systemsimilaritytagtime factor

References

[1]
G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734-749, 2005.
Crossref Google Scholar
[2]
L. Y. Lü, M. Medo, C. H. Yeung, Y. C. Zhang, Z. K. Zhang, and T. Zhou, Recommender systems, Phys. Rep., vol. 519, no. 1, pp. 1-49, 2012.
Crossref Google Scholar
[3]
G. Linden, B. Smith, and J. York, Amazon.Com recommendations: Item-to-Item collaborative filtering, IEEE Int. Comput., vol. 7, no. 1, pp. 76-80, 2003.
Crossref Google Scholar
[4]
O. Celma and X. Serra, FOAFing the music: Bridging the semantic gap in music recommendation, Web Semant., vol. 6, no. 4, pp. 250-256, 2008.
Crossref Google Scholar
[5]
F. Hopfgartner, T. Brodt, J. Seiler, B. Kille, A. Lommatzsch, M. Larson, R. Turrin, and A. Serény, Benchmarking news recommendations: The CLEF NewsREEL use case, ACM SIGIR Forum, vol. 49, no. 2, pp. 129-136, 2015.
Crossref Google Scholar
[6]
M. Balabanović and Y. Shoham, Combining content-based and collaborative recommendation, Commun. ACM, vol. 40, no. 3, pp. 66-72, 1997.
Crossref Google Scholar
[7]
J. B. Shu, X. X. Shen, H. Liu, B. L. Yi, and Z. L. Zhang, A content-based recommendation algorithm for learning resources, Multimed. Syst., .
Crossref Google Scholar
[8]
Y. Koren, Factorization meets the neighborhood: A multifaceted collaborative filtering model, in Proc. 14th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Las Vegas, NV, USA, 2008, pp. 426-434.
Crossref
[9]
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, Item-based collaborative filtering recommendation algorithms, in Proc. 10th Int. Conf. World Wide Web, Hong Kong, China, 2001, pp. 285-295.
Crossref
[10]
A. Felfernig, Koba4MS: Selling complex products and services using knowledge-based recommender technologies, in Proc. 7th IEEE Int. Conf. E-Commerce Technology, Munich, Germany, 2005, pp. 92-100.
[11]
A. Felfernig and K. Shchekotykhin, Debugging user interface descriptions of knowledge-based recommender applications in Proc. 11st Int. Conf. Intelligent User Interfaces, Sydney, Australia, 2006, pp. 234-241.
Crossref
[12]
J. Wang, A. P. De Vries, and M. J. T. Reinders, Unifying user-based and item-based collaborative filtering approaches by similarity fusion, in Proc. 29th Annual Int. ACM SIGIR Conf. Research and Development in Information Retrieval, Seattle, WA, USA, 2006, pp. 501-508.
Crossref
[13]
B. L. Wang, J. H. Huang, L. B. Ou, and R. Wang, A collaborative filtering algorithm fusing user-based, item-based and social networks, in Proc. 2015 IEEE Int. Conf. Big Data, Santa Clara, CA, USA, 2015, pp. 2337-2343.
Crossref
[14]
H. Koohi and K. Kiani, User based collaborative filtering using fuzzy c-means, Measurement, vol. 91, pp. 134-139, 2016.
Crossref Google Scholar
[15]
X. Y. Liu, C. Aggarwal, Y. F. Li, X. N. Kong, X. Y. Sun, and S. Sathe, Kernelized matrix factorization for collaborative filtering, in Proc. 2016 Siam Int. Conf. Data Mining, Miami, FL, USA, 2016, pp. 378-386.
Crossref
[16]
T. Hofmann, Latent semantic models for collaborative filtering, ACM Trans. Inf. Syst., vol. 22, no. 1, pp. 89-115, 2004.
Crossref Google Scholar
[17]
R. Salakhutdinov, A. Mnih, and G. Hinton, Restricted Boltzmann machines for collaborative filtering, in Proc. 24th Int. Conf. Machine Learning, Corvalis, OR, USA, 2007, pp. 791-798.
Crossref
[18]
H. L. Xu, X. Wu, X. D. Li, and B. P. Yan, Comparison Study of internet recommendation system, (in Chinese), J. Softw., vol. 20, no. 2, pp. 350-362, 2009.
Crossref Google Scholar
[19]
M. K. Najafabadi, M. N. Mahrin, S. Chuprat, and H. M. Sarkan, Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data, Comput. Hum. Behav., vol. 67, pp. 113-128, 2017.
Crossref Google Scholar
[20]
N. Gao and M. Yang, An improved unifying tags and ratings collaborative filtering for recommendation system, (in Chinese), J. Nanjing Nor. Univ. (Nat. Sci. Ed.), vol. 38, no. 1, pp. 98-103, 2015.
Google Scholar
[21]
H. Koohi and K. Kiani, A new method to find neighbor users that improves the performance of collaborative filtering, Expert Syst. Appl., vol. 83, no. C, pp. 30-39, 2017.
Crossref Google Scholar
[22]
D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, Using collaborative filtering to weave an information tapestry, Commun. ACM, vol. 35, no. 12, pp. 61-70, 1992.
Crossref Google Scholar
[23]
Z. D. Zhao and M. S. Shang, User-based collaborative-filtering recommendation algorithms on hadoop, in Proc. 3rd Int. Conf. Knowledge Discovery and Data Mining, Phuket, Thailand, 2010, 478-481.
[24]
H. Ji, J. F. Li, C. R. Ren, and M. He, Hybrid collaborative filtering model for improved recommendation, in Proc. 2013 IEEE Int. Conf. Service Operations and Logistics, and Informatics, Dongguan, China, 2013.
Crossref
[25]
S. Y. Wei, N. Ye, S. Zhang, X. Huang, and J. Zhu, Item-based collaborative filtering recommendation algorithm combining item category with interestingness measure, in Proc. 2012 Int. Conf. Computer Science and Service System, Nanjing, China, 2012, pp. 2038-2041.
Crossref
[26]
G. Karypis, Evaluation of item-based top-N recommendation algorithms, in Proc. 10th Int. Conf. Information and Knowledge Management, Atlanta, GA, USA, 2001, pp. 247-254.
Crossref
[27]
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, GroupLens: An open architecture for collaborative filtering of Netnews, in Proc. 1994 ACM Conf. Computer Supported Cooperative Work, Chapel Hill, NC, USA, 1994, pp. 175-186.
Crossref
Big Data Mining and Analytics
Volume 1 Issue 2,
June 2018
Pages 128-136
DOI: 10.26599/BDMA.2018.9020012
Cite this article:
Zhang C, Yang M, Lv J, et al. An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor. Big Data Mining and Analytics, 2018, 1(2): 128-136. https://doi.org/10.26599/BDMA.2018.9020012

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Received: 27 December 2017
Accepted: 03 January 2018
Published: 12 April 2018
© The author(s) 2018
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