计算机技术与发展2025，Vol.35Issue(3)：172-178,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0335
基于一维卷积神经网络的序列推荐算法
Sequence Recommendation Algorithm Based on One-dimensional Convolutional Neural Network
摘要
Abstract
In recent years,recommendation algorithms have become a key solution to the problem of information overload,providing users with personalized content effectively.In the field of sequential recommendation,convolutional neural networks have gained widespread attention for their ability to extract local features from sequential information.However,convolutional neural networks have limitations in capturing temporal information.To address this issue,we propose a temporal recommendation algorithm based on one-dimensional con-volution.The proposed algorithm first extracts local features from the sequence through convolution operations,then extracts long-term features through pooling operations,and combines the two using weighted addition to effectively capture both local and long-term features.Next,the extracted information is multiplied pointwise with linearly transformed sequential information to introduce temporal in-formation.Additionally,a feedforward network is used to achieve nonlinear transformation and enhance cross-dimensional interactions.Finally,the algorithm calculates the correlation between user feature vectors and item feature vectors to make recommendations.Experimental results show that in tests on the MovieLens movie dataset and the KuaiRand short video dataset,the proposed algorithm sig-nificantly improves metrics such as hit rate and normalized discounted cumulative gain compared to four baseline algorithms.It is demon-strated that the proposed algorithm is more effective in making recommendations.关键词
推荐算法/序列推荐/卷积神经网络/前馈网络/用户特征Key words
recommendation algorithm/sequential recommendation/convolutional neural network/feedforward network/user feature分类
计算机与自动化引用本文复制引用
黄康鹏,冯锋..基于一维卷积神经网络的序列推荐算法[J].计算机技术与发展,2025,35(3):172-178,7.基金项目
宁夏重点研发计划重点项目(2022BEG02016) （2022BEG02016）
宁夏自然科学基金项目(2023AAC03031) （2023AAC03031）
