《计算机应用研究》|Application Research of Computers

一种结合主题模型的推荐算法

Recommendation algorithm combining theme model

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作者 曹占伟,胡晓鹏
机构 西南交通大学 信息科学与技术学院,成都 611756
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文章编号 1001-3695(2019)06-008-1638-05
DOI 10.19734/j.issn.1001-3695.2017.12.0811
摘要 针对传统协同过滤推荐算法存在的冷启动、数据稀疏以及相似度度量的准确性问题,基于LDA主题模型对文本隐式主题挖掘的有效性和KL散度在主题分布相似性度量的准确性,提出了结合LDA主题模型的矩阵分解推荐算法。首先,利用改进的LDA算法输出项目—主题分布,并用困惑度作为主题数设置的修正函数;然后分别基于余弦相似度和KL散度计算得到项目相似度矩阵,将得到的相似度矩阵结合原评分训练集输出预评分,再将预评分填充到训练集;最后将训练集输入ALS矩阵分解算法得到推荐结果。通过MovieLens数据集的实验结果表明,该算法在不同隐式参数设定下均能得到比ALS推荐算法以及更小的预测误差,并且最优预测误差小于传统推荐算法。该实验说明了通过集成LDA主题模型的ALS算法效果要优于其他推荐算法。
关键词 推荐算法; 矩阵分解; 隐式狄利克雷分布; KL散度; 主题模型
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本文URL http://www.arocmag.com/article/01-2019-06-008.html
英文标题 Recommendation algorithm combining theme model
作者英文名 Cao Zhanwei, Hu Xiaopeng
机构英文名 School of Information Science & Technology,Southwest Jiaotong University,Chengdu 611756,China
英文摘要 In order to solve the problem of cold start and data sparsity for traditional collaborative filtering recommendation algorithm, and the accuracy of similarity measurement, this paper proposed a matrix decomposition recommendation algorithm based on the LDA theme model. Firstly, it used the improved LDA algorithm to output the project-topic distribution, using the perplexity as the modified function of the subject number. Secondly, it calculated the similarity matrix of the project based on the cosine similarity and the KL divergence, combining the obtained similarity matrix with the original scoring training set to output the pre score, and then filled the preliminary score to the training set. Finally, it input the training set to ALS matrix decomposition algorithm to get the recommended results. The experimental results of the MovieLens data set show that the proposed algorithm can get a smaller MAE values than the traditional ALS algorithm under different implicit parameter settings and it greater than traditional recommdation algorithm. The experiment shows that the results of the ALS algorithm are better than other algorithms by integrating the LDA theme model.
英文关键词 recommendation algorithm; matrix decomposition; latent Dirichlet distribution(LDA); KL divergence; theme model
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收稿日期 2017/12/19
修回日期 2018/2/7
页码 1638-1642
中图分类号 TP301.6
文献标志码 A