Improved Network-Based Recommendation Algorithm

Chuanmin  Mi; Xiaofei  Shan; Jing  Ma

doi:10.4236/ib.2014.63012

Chuanmin Mi^*, Xiaofei Shan, Jing Ma

Abstract: Recently, personalized recommender systems have become indispensable in a wide variety of commercial applications due to the vast amount of overloaded information. Network-based recommendation algorithms for user-object link predictions have achieved significant developments. But most previous researches on network-based algorithm tend to ignore users’ explicit ratings for objects or only select users’ higher ratings which lead to the loss of information and even sparser data. With this understanding, we propose an improved network-based recommendation algorithm. In the process of reallocation of user’s recommendation power, this paper originally transfers users’ explicit scores to users’ interest similarity and user’s representativeness. Finally, we validate the proposed approach by performing large-scale random sub-sampling experiments on a widely used data set (Movielens) and compare our method with two other algorithms by two accuracy criteria. Results show that our approach significantly outperforms other algorithms.

Keywords: Recommendation, Network-Based, Source Reallocation, Rank Score, Hitting Rate

Cite this paper: Mi, C. , Shan, X. and Ma, J. (2014) Improved Network-Based Recommendation Algorithm. iBusiness, 6, 109-116. doi: 10.4236/ib.2014.63012.

References

[1] Huang, Z., Zeng, D.D. and Chen, H. (2007) Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems. Management Science, 53, 1146-1164. http://dx.doi.org/10.1287/mnsc.1060.0619

[2] Lü, L.-Y. and Zhou, T. (2011) Link Prediction in Complex Networks: A Survey. Physica A: Statistical Mechanics and Its Applications, 390, 1150-1170.
http://dx.doi.org/10.1016/j.physa.2010.11.027

[3] Sarukkai, R.R. (2000) Link Prediction and Path Analysis Using Markov Chains. Computer Networks, 33, 377-386.
http://dx.doi.org/10.1016/S1389-1286(00)00044-X

[4] Liben-Nowell, D. and Kleinberg, J. (2007) The Link-Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology, 58, 1019-1031.
http://dx.doi.org/10.1002/asi.20591

[5] Zhou, T., Kuscsik, Z., Liu, J.G., et al. (2010) Solving the Apparent Diversity-Accuracy Dilemma of Recommender Systems. Proceedings of the National Academy of Sciences of the United States of America, 107, 4511-4515.
http://dx.doi.org/10.1073/pnas.1000488107

[6] Zhou, T., Ren, J., Medo, M. and Zhang, Y.-C. (2007) Bipartite Network Projection and Personal Recommendation. Physical Review E, 76, 1-7.
http://dx.doi.org/10.1103/PhysRevE.76.046115

[7] Zhou, T., Jiang, L.L., Su, R.Q., et al. (2008) Effect of Initial Configuration on Network-Based Recommendation. Europhysics Letters, 81, 1-4.
http://dx.doi.org/10.1209/0295-5075/81/58004

[8] Zhou, T., Su, R.Q., Liu, R.R., et al. (2009) Accurate and Diverse Recommendations via Eliminating Redundant Correlations. New Journal of Physics, 11, 1-19.
http://dx.doi.org/10.1088/1367-2630/11/12/123008

[9] Liu, C. and Zhou, W.X. (2012) Heterogeneity in Initial Resource Configurations Improves a Network-Based Hybrid Recommendation Algorithm. Physica A: Statistical Mechanics and Its Applications, 391, 5704-5711.
http://dx.doi.org/10.1016/j.physa.2012.06.034

[10] Guan, Y., Zhao, D.D., Zeng, A. and Shang, M.-S. (2013) Preference of Online Users and Personalized Recommendations. Physica A: Statistical Mechanics and Its Applications, 392, 3417-3423.
http://dx.doi.org/10.1016/j.physa.2013.03.045

[11] Wang, Q. and Duan S.Y. (2013) Improved Recommendation Algorithm Based on Bipartite Networks. Application Research of Computers, 30, 771-775.

[12] Herlocker, J.L., Konstan, J.A., Terveen, K., et al. (2004) Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information and Systems, 22, 5-53.
http://dx.doi.org/10.1145/963770.963772

[13] Billsus, D. and Pazzani, M.J. (1998) Learning Collaborative Information Filters. Proceedings of the 15th International Conference on Machine Learning, Madison, 26-30 July 1998, 46-54.

[14] Linden, G. (2009) What Is a Good Recommendation Algorithm.
http://cacm.acm.org/blogs/blog-cacm/22925-what-is-a-good-recommendation-algorithm/fulltext.

[15] Wei, W., Liu, Q. and Zhang, L. (2013) Review on Diversity in Personalized Recommender Systems. Library and Information Service, 57, 127-136.