论文信息 - An Expected Item Bias based Hybrid Approach for Recommendation System 1

An Expected Item Bias based Hybrid Approach for Recommendation System 1

To improve the accuracy of memory based recommendation while keeping the low time cost, an expected item bias (EIA) based similarity computation is proposed. And a hybrid approach (HA) integrating the global rating information and local rating information is also proposed. The features of two classical datasets MovieLens and Netflix for recommendation system benchmarking are anglicized. The experiments on MovieLens and Netflix show that both EIA and HA could improve the performance alone. A combinational use of them will lead even better results on the two benchmark datasets.

Fan Li | Changan Yuan | Kaikuo Xu | Xianbin Liu

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