基于矩阵分解的社交网络正则化推荐模型
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国家自然科学基金(61304237)


Recommendation Model of Matrix Factorization Based on Social Network Regularization
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    摘要:

    社交网站的快速发展和普及使得实现高效的好友推荐成为了一个热点问题,而矩阵分解算法是被业界广泛采用的方法.虽然传统的矩阵分解算法能够带来良好的效果,但是仍然存在一些问题.首先,算法没有充分利用用户之间的社交网络结构化关系;其次,算法依赖的用户-物品评分矩阵只有二级评分不能充分表达用户的喜好.提出了一种基于矩阵分解的社交网络正则化推荐模型,利用社交网络中用户的近邻关系进行建模,并将其作为一种辅助信息融合到矩阵分解模型当中,该模型能够解决传统矩阵分解面临的问题.通过在腾讯微博数据集上进行实验对比,验证了本文提出的方法与传统的推荐方法相比能取得更高的推荐平均准确度.

    Abstract:

    With the rapid development and popularization of social network site, how to achieve efficient friend recommendation has become a hot issue. Currently, Matrix Factorization algorithm is widely used method by industry. Although the traditional Matrix Factorization algorithm could bring a good results, but there are still some problems. First, this model does not take full advantage of structural relationship between users in social network; Secondly, this algorithm is dependent on the user-rating matrix, which only has secondary scoring and cannot fully express the user's preferences. In order to solve these two problems, a Matrix Factorization model with social network regularization was proposed in this paper, modeling use of social network users in the model the relationship between neighbors. And as an auxiliary information fusion to the matrix Decomposition Model. This?model?can?solve?the?problems?that?traditional?Matrix Factorization model cannot?solve. Though the contrast experiments on tencent weibo data set, verify that our proposed method could obtain a higher mean average precision than other traditional methods.

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林晓勇,代苓苓,史晟辉,李芳.基于矩阵分解的社交网络正则化推荐模型.计算机系统应用,2016,25(1):9-16

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  • 收稿日期:2015-04-16
  • 最后修改日期:2015-05-15
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  • 在线发布日期: 2016-01-15
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