An Improved Recommender Model by Joint Learning of Both Similarity and Latent Feature Space
An Improved Recommender Model by Joint Learning of Both Similarity and Latent Feature Space
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相似性和潜在特征空间联合学习的改进推荐模型
DOI：
10.1007/978-3-319-46257-8_40
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发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Ming Yang
中科院分区：
文献类型：
--
作者：
Yunxiang Tao;Ming Yang
The matrix factorization recommender system based on manifold regularization, taking into account the similarity of local neighbors and manifold structure, can improve the quality of a recommendation system. However, the similarity between samples may not be accurate due to the sparsity of the data or the incompleteness of the tag information. Therefore, we propose a new model called SI-GMF (Similarity-learning-based Improved Graph Regularized matrix Factorization) by embedding the new similarity measure strategy in GMF (Graph Regularized matrix Factorization) framework, and induce three new matrix factorization algorithms (SI-GMF_1, SI-GMF_2, SI-GMF_3) based on three initial similarities by employing three different similarity measures. The solutions to the newly developed algorithms can be effectively obtained by SGD method. The experimental results show that the newly designed algorithms significantly improve the accuracy of a recommender system.
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