Matrix factorization based social recommender model

YAN Surong; FENG Xiaoqing; LIAO Yixing

doi:10.16511/j.cnki.qhdxxb.2016.21.045

PDF(1048 KB)

Journal of Tsinghua University(Science and Technology) ›› 2016, Vol. 56 ›› Issue (7) : 793-800. DOI: 10.16511/j.cnki.qhdxxb.2016.21.045

COMPUTER SCIENCE AND TECHNOLOGY

Matrix factorization based social recommender model

{{article.zuoZhe_EN}}

Author information +

History +

Abstract

This study describes an improved matrix factorization based social recommender model that uses tailored relationship networks of users as a solution for the sparsity, cold-start and scalability problems in big datasets. The social influence of the relationship networks is targeted as an extra user-item specific bias for the matrix factorization with the uniformity of relationship networks modeled as dynamic social regularization terms in the matrix factorization. A boosting-shrinking algorithm is used for the relationship networks for better prediction accuracy and scalability where the relationships of each user are tailored to generate personalized relationship networks according to the user-specific data density of the user-item rating matrix and the correlation matrix. Tests on unbalanced datasets with different sparsity levels show that this model significantly improves the prediction accuracy for sparse datasets, effectively addresses the cold-start problem, and has better scalability compared to other state-of-the-art matrix factorization based social recommendation models.

Key words

big data / social networks / matrix factorization / sparsity / scalability

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

YAN Surong, FENG Xiaoqing, LIAO Yixing. Matrix factorization based social recommender model[J]. Journal of Tsinghua University(Science and Technology). 2016, 56(7): 793-800 https://doi.org/10.16511/j.cnki.qhdxxb.2016.21.045

References

[1] Koren Y. Factorization meets the neighborhood:A multifaceted collaborative filtering model[C]//Proc ACM SIGKDD'08. Las Vegas, NE, USA:ACM, 2008:426-434.
[2] Su X, Khoshgoftaar T. A survey of collaborative filtering techniques[J]. Advances in Artificial Intelligence, 2009(2009), 421425.
[3] Tavakolifard M, Almeroth K C. Social computing:an intersection of recommender systems, trust/reputation systems, and social networks[J]. Network, IEEE, 2012, 26(4):53-58.
[4] Xin J C, Wang Z Q, Qu L X, et al. Elastic extreme learning machine for big data classification[J]. Neuro computing, 2015(149):464-471.
[5] Jamiy E L, Daif A, Azouazi M, et al. The potential and challenges of Big data-Recommendation systems next level application[J]. International Journal of Computer Science Issues, 2014, 11(5):21-26.
[6] Pagare R, Patil S A. Study of collaborative filtering recommendation algorithm-scalability issue[J]. International Journal of Computer Applications, 2013, 67(25):10-15
[7] McPherson M, Smith-Lovin L, Cook J. Birds of a feather:Homophily in social networks[J]. Annual review of sociology, 2001, 27:415-444.
[8] Marsden P, Friedkin N. Network studies of social influence[J]. Sociological Methods and Research, 1993, 22(1):127-151.
[9] Liu LY, Medob M, Yeung CH, et al. Recommender systems[J]. Physics Reports, 2012, 59(1):1-49.
[10] Ma H, Yang H, Lyu M R, et al. SoRec:Social recommendation using probabilistic matrix factorization[C]//Proc ACM IKM'08. Napa Valley, CA, USA:ACM, 2008:931-940.
[11] Ma H, King I, Lyu M R. Learning to recommend with social trust ensemble[C]//Proc ACM SIGIR'09. Boston, MA, USA:2009:203-210.
[12] Ma H, Zhou D, Liu C, et al. Recommender systems withsocial regularization[C]//Proc ACM WSMD'11. Hong Kong, China:ACM, 2011:287-296.
[13] Jamali M, Ester M. Trustwalker:a random walk model for combining trust-based and item-based recommendation[C]//Proc ACM SIGKDD'09. Paris, France:ACM, 2009:397-406.
[14] Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks[C]//Proc ACM RecSys'10. Barcelona, Spain:ACM, 2010:135-142.
[15] Symeonidis P, Tiakas E, Manolopoulos Y. Product recommendation and rating prediction based on multi-modal social networks[C]//Proc ACM RecSys'11. Chicago, IL, USA:ACM, 2011:61-68.
[16] Yuan Q, Chen L, Zhao S. Factorization vs. regularization:fusing heterogeneous social relationships in top-n recommendation[C]//Proc ACM RecSys'11. Chicago, IL, USA:ACM, 2011:245-252.
[17] Yang X, Steck H, Liu Y. Circle-based recommendation in online social networks[C]//Proc ACM SIGKDD'12. Beijing, China:ACM, 2012:1267-1275.
[18] Noel J, Sanner S, Tran K N, et al. Objective functions for social collaborative filtering[C]//Proc WWW'12. Lyon, France, 2012:859-868.
[19] Yan S R, Zheng X L, Chen D R, et al. Exploiting two-faceted web of trust for enhanced-quality recommendations[J]. Expert Systems with Applications, 2013, 40(17):7080-7095.
[20] Chen C, Zheng X, Wang Y, et al. Context-aware collaborative topic regression with social matrix factorization for recommender systems[C]//Proc AAAI'14. Québec City, QU, Canada, 2014:9-15.
[21] Tang J, Hu X, Liu H. Social recommendation:A review[J]. Social network analysis and mining, 2013, 3(4):1113-1133.
[22] Colombo-Mendoza L O, Valencia-García R, Rodríguez-González A, et al. RecomMetz:A context-aware knowledge-based mobile recommender system for movie show times[J]. Expert Systems with Applications, 2015, 42(3):1202-1222.
[23] Liu X, Aberer K. SoCo:A social network aided context-aware recommender system[C]//Proc www'13. Rio de Janeiro, Brazil, 2013:781-802.
[24] Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8):30-37.
[25] Salakhutdinov R, Mnih A. Probabilistic matrix factorization[J]. Advances in neural information processing systems, 2008, 20(1):1257-1264.
[26] Menon A K, Elkan C. A log-linear model with latent features for dyadic prediction[C]//Proc ICDM'10. Sydney, Australia:IEEE, 2010:364-373.
[27] Yan S R, Zheng X L, Chen D R, et al. User-centric trust and reputation model for personal and trusted service selection[J]. International Journal of Intelligent Systems, 2011, 26(8):687-717.
[28] Yan S R, Zheng X L, Wang Y, et al. Graph-based comprehensive reputation model:Exploiting the social context of opinions to enhance trust in social commerce[J]. Information Sciences, 2015, 318:51-72.
[29] Gantner Z, Rendle S, Freudenthaler C, et al. MyMediaLite:A Free Recommender System Library[C]//Proc ACM RecSys'11. Chicago, IL, USA:ACM, 2011:305-308.
[30] Wang J, Zhang Y. Utilizing marginal net utility for recommendation in e-commerce[C]//Proc ACM SIGIR'11. Beijing, China:ACM, 2011:1003-1012.
[31] Wang J, Zhang Y. Opportunity model for e-commerce recommendation:Right product; right time[C]//Proc ACM SIGIR' 13. Dublin, Ireland:ACM, 2013:303-312.