Hybrid Recommendation Algorithm Based on Autoencoder and Clustering

ZHAO Yang; ZHU Quan-Yin; HU Rong-Lin; QU Xue-Xin

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Hybrid Recommendation Algorithm Based on Autoencoder and Clustering

更新时间：2026-09-04

- Hybrid Recommendation Algorithm Based on Autoencoder and Clustering
- Vol. 35, Issue 11, Pages: 52-56(2018)
- 作者机构：
  
  1. 南京工业大学机构计算机科学与技术学院,江苏,南京,211816
  2. 淮阴工学院机构计算机与软件工程系,江苏,淮安,223003
- 作者简介：
- 基金信息：
- DOI：
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- Published：2018
- 稿件说明：
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赵阳, 朱全银, 胡荣林, 瞿学新. 基于自编码机和聚类的混合推荐算法[J]. 微电子学与计算机, 2018, 35(11): 52-56.

ZHAO Yang, ZHU Quan-Yin, HU Rong-Lin, et al. Hybrid Recommendation Algorithm Based on Autoencoder and Clustering[J]. 2018, 35(11): 52-56.
赵阳, 朱全银, 胡荣林, 瞿学新. 基于自编码机和聚类的混合推荐算法[J]. 微电子学与计算机, 2018, 35(11): 52-56. DOI：

ZHAO Yang, ZHU Quan-Yin, HU Rong-Lin, et al. Hybrid Recommendation Algorithm Based on Autoencoder and Clustering[J]. 2018, 35(11): 52-56. DOI：

摘要

针对传统的协同过滤推荐算法在稀疏数据集上表现不佳的情况

提出了一种将自编码机(Autoencoder)和聚类结合的混合推荐算法.首先将用户项目评分数据和用户人口统计学数据作为自编码机的输入

提取用户特征.然后利用提取到的用户特征对用户聚类得到用户类别

从而使近邻搜索范围减小.接着通过计算平均绝对误差(MAE)寻找到适用于同一类别用户的推荐算法

最后组合各类别上适用的推荐算法

得到混合推荐模型.推荐阶段

计算目标用户类别

并使用混合推荐模型得到推荐结果.实验结果表明

该算法可以有效的提高推荐质量。

Abstract

With the problem that traditional collaborative filtering recommendation algorithm does not recommend high quality on sparse data sets

a hybrid recommendation algorithm that based on autoencoder and clustering is proposed. First

the users project rating data and user demographic data as the input data from the autoencoder to extract user characteristics. Then use the extracted user characteristics to cluster the users to obtain the user categories

so that the search range of the neighbor decreases. Then search for a recommendation algorithm for the same type of users by calculating mean absolute error (MAE)

and get a hybrid recommendation model. Finally calculate the target user category and use the hybrid recommendation model to get the recommended results. Experimental results show that this algorithm can effectively improve the quality of recommendation.

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Keywords

references

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朱东郡, 李敬兆, 谭大禹, 等.基于标签聚类和兴趣划分的协同过滤推荐算法[J].计算机工程, 2017, 43 (11) :146-151. DOI: 146

王志虎, 黄曼莹.基于用户历史行为的协同过滤推荐算法[J].微电子学与计算机, 2017, 34(5):132-136. DOI: 132

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