Customer Rating Prediction Using Hypergraph Kernel Based Classification
Recommender systems in online marketing websites like Amazon.com and CDNow.com suggest relevant services and favorite products to customers. In this paper, we proposed a novel hypergraph-based kernel computation combined with k nearest neighbor (kNN) to predict ratings of users. In this method, we change regular definition style of hypergraph diffusion kernel. Our comparative studies show that our method performs better than typical kNN, which is simple and appropriate for online recommending applications.
KeywordsRecommender System Rating Prediction Classification Hypergraph Kernel
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- 1.Debnath, S., Ganguly, N., Mitra, P.: Feature weighting in content based recommendation system using social network analysis. In: Proceedings of the 17th International Conference on World Wide Web, pp. 1041–1042. ACM (2008)Google Scholar
- 3.Julashokri, M., Fathian, M., Gholamian, M.: Improving customer’s profile in recommender systems using time context and group preferences. In: The 5th International Conference on Computer Sciences and Convergence Information Technology (ICCIT), pp. 125–129 (2010)Google Scholar
- 4.Scholkopf, B., Smola, A.: Learning with kernels. MIT Press (2001)Google Scholar
- 5.Cook, D., Holder, L.: Mining Graph Data. Wiley-Interscience (2007)Google Scholar
- 6.Berge, C.: Graphs and Hypergraphs. North-Holland Publishing Company (1973)Google Scholar