Abstract
Evaluation and recommendation are different actions, but they are consistent in mining and using information efficiently and effectively to improve their persuasiveness and accuracy. From the view of information processing, the paper builds a two-dimensional graph model which expresses the relationships between evaluators and objects. This graph model reflects the original information of evaluation or recommendation systems and has its equivalent matrix form. Next, the principle of matrix projection can be applied to get the evaluation or recommendation vector by solving the matrix maximization problems. What’s more, a rating data set of online move is selected to verify the model and method. In conclusion, from the example analysis, it is found that the proposed evaluation method is reasonable, and from the numerical experimental comparison, the proposed recommendation method is proved to be time-saving and more accurate than the generally adopted recommendation methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sulin, B., Paul, A.P.: Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior. MIS Quarterly 26(3), 243–268 (2002)
Paul, A.P., David, G.: Building Effective Online Marketplaces with institution-Based Trust. Information Systems Research 15(1), 37–59 (2004)
Paul, A.P., Liang, H., Xue, Y.J.: Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Quarterly 31(1), 105–136 (2007)
Park, D.H., Kim, S.: The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electronic Commerce Research and Applications 7(4), 399–410 (2008)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Asela, G., Guy, S.: A Survey of Accuracy Evaluation Metrics of Recommenda- tion Tasks. The Journal of Machine Learning Research 10(12), 2935–2962 (2009)
Zhang, Z.K., Zhou, T., Zhang, Y.C.: Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs. Physica A: Statistical Mechanics and its Applications 389(1), 179–186 (2010)
Zhang, Y.C., Medo, M., Ren, J.: Recommendation model based on opinion diffusion. Europhysics Letters 80(6), 68003 (2007)
Azene, Z., Anthony, F.N.: Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets and Systems 160(1), 76–94 (2009)
Liu, R.R., Jia, C.X., Zhou, T.: Personal recommendation via modified collaborative filtering. Physica A: Statistical Mechanics and its Applications 388(4), 462–468 (2009)
Panagiotis, S., Alexandros, N., Apostolos, N.P.: Nearest-balusters’ collaborative filtering based on constant and coherent values. Information Retrieval 11(1), 51–75 (2008)
Herlocker, J.L., Konstan, J.A., Riedl, J.: An Empirical Analysis of Design Choices in Neighborhood-based Collaborative Filtering Algorithms. Information Retrieval 5, 287–310 (2002)
Shang, M.S., Lü, L., Zeng, W.: Relevance is more significant than correlation: Information filtering on sparse data. Europhysics Letters 88(6), 68008 (2009)
Zhou, T., Su, R.Q., Liu, R.R.: Accurate and diverse recommendations via eliminating redundant correlations. New Journal of Physics 11(12), 123008 (2009)
Liu, J.G., Zhou, T., Wang, B.H.: Degree Correlation of Bipartite Network on Personalized Recommendation. International Journal of Modern Physics C 21(1), 137–147 (2010)
Pan, X., Deng, G.S., Liu, J.G.: Information Filtering via Improved Similarity Definition. Chin. Phys. Lett. 27(6), 68903 (2010)
Richard, B.: Introduction to matrix analysis. McGraw-Hill Book Company, New York (1970)
David, G.L.: Introduction to Dynamic Systems: Theory, Models and Applications. John Wiley & Sons, Chichester (1979)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Y., Sun, J., Wang, K., Zheng, A. (2011). Evaluation and Recommendation Methods Based on Graph Model. In: Hu, B., Liu, J., Chen, L., Zhong, N. (eds) Brain Informatics. BI 2011. Lecture Notes in Computer Science(), vol 6889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23605-1_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-23605-1_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23604-4
Online ISBN: 978-3-642-23605-1
eBook Packages: Computer ScienceComputer Science (R0)