广西师范大学学报（自然科学版）2024，Vol.42Issue(5)：91-100,10.DOI:10.16088/j.issn.1001-6600.2023110603
基于用户行为特征的深度混合推荐算法
A Deep Hybrid Recommendation Algorithm Based on User Behavior Characteristics
摘要
Abstract
The hybrid recommendation model,named IEU-DeepCFM(deep and cross factorization machine information extraction unit),is proposed in this paper,which is based on the deep factorization machine and integrates the information extraction unit and cross network structure.In the proposed model,a fixed representation of each feature is learned by most existing recommendation methods.However,it is recognized that user behavioral preferences change with contextual features,and features have different importance in different contexts.Therefore,inaccurate recommendation results may be caused by the fixed representation of features given by the model.To address this issue,the information extraction unit module is introduced,consisting of a self-attention mechanism and a contextual information extractor.This module learns context-aware feature representations for each feature in various contexts.Subsequently,a deep cross factorization machine is employed to mine low-and high-order features of the user.This enables users to receive more explicit cross-information,ultimately leading to click-through rate predictions based on user behavioral characteristics.The results of ablation and comparison experiments conducted on the MovieLens movie dataset and the Avazu advertising click-through rate dataset demonstrate the improvement in both AUC and LogLoss indicators achieved by the proposed model.This confirms the rationality of the model.关键词
深度学习/上下文特征/信息提取单元/推荐算法/自注意力机制Key words
deep learning/contextual features/information extraction unit/recommendation algorithm/self-attention mechanism分类
信息技术与安全科学引用本文复制引用
杜帅文,靳婷..基于用户行为特征的深度混合推荐算法[J].广西师范大学学报（自然科学版）,2024,42(5):91-100,10.基金项目
国家自然科学基金(61862021) （61862021）
海南省自然科学基金(620RC565) （620RC565）
