南京理工大学学报（自然科学版）2024，Vol.48Issue(4)：460-468,9.DOI:10.14177/j.cnki.32-1397n.2024.48.04.007
基于深度特征融合的协同推荐算法
Collaborative recommendation algorithm based on deep feature fusion
王成1
作者信息
- 1. 南京理工大学 计算机科学与工程学院,江苏 南京 210094
- 折叠
摘要
Abstract
Deep neural networks faces challenges such as data sparsity and low recommendation accuracy.Therefore,a collaborative recommendation algorithm based on deep feature fusion is proposed to improve the problem by integrating deep neural networks with collaborative filtering algorithms.Firstly,a quadratic polynomial regression model is used to extract features from a user item rating matrix;secondly,using deep neural networks to train the input latent features and generate user item ratings;finally,the recommendation candidate set generated by the term frequency-inverse file frequency algorithm is used to fuse user item ratings and output the recommendation results.Using MovieLens rating data for experiments,the mean absolute error(MAE)and root mean square error(RMSE)of the hybrid recommendation algorithm in this paper are 0.745 9 and 0.888 6,respectively,which are 14.143%and 24.341%higher than traditional deep neural networks,and also better than the mixed recommendation model in the control group.关键词
深度神经网络/二次多项式/词频-逆向文件频率/特征融合/相似度Key words
deep neural network/quadratic polynomial/term frequency-inverse document frequency/feature fusion/similarity分类
信息技术与安全科学引用本文复制引用
王成..基于深度特征融合的协同推荐算法[J].南京理工大学学报（自然科学版）,2024,48(4):460-468,9.
