计算机与数字工程2026，Vol.54Issue(3)：623-629,639,8.DOI:10.3969/j.issn.1672-9722.2026.03.007
基于辅助信息特征融合的序列推荐算法
Sequential Recommendation Algorithm Based on Auxiliary Information Feature Fusion
摘要
Abstract
The sequential recommendation predicts the user's future action based on the user's historical behavior.In order to improve the accuracy of next-item prediction,the modeling information includes the product category and other auxiliary informa-tion.The inner correlation of auxiliary information is ignored because the current algorithm fuses the sequential information and aux-iliary information earlier.In response to the above problems,this study proposes a Auxiliary Information Feature Fusion for Sequen-tial Recommendation(AIFF),which uses the attention mechanism to extract the inherent multi-granularity relationship of auxiliary information.The fused auxiliary information representation is used as the attention weight.And the sequential information is used as the value of attention.This paper inputs it into attention mechanism and neural network to fuse sequence information to make recom-mendations.The contrast experiment on three recommendation datasets shows AIFF has achieved good experimental results.At the same time,the universality experiment shows that the auxiliary information feature fusion solution can be incorporated into other at-tention sequential recommendations.关键词
辅助信息融合/序列推荐算法/注意力机制Key words
auxiliary information fusion/sequential recommendation algorithm/attention mechanism分类
信息技术与安全科学引用本文复制引用
赵铁柱,周志强,杨秋鸿..基于辅助信息特征融合的序列推荐算法[J].计算机与数字工程,2026,54(3):623-629,639,8.基金项目
广东省普通高校重点领域专项(编号:2021ZDZX3007) （编号:2021ZDZX3007）
东莞城市学院青年教师发展基金项目(编号:2022QJY005Z)资助. （编号:2022QJY005Z）
