郑州大学学报（工学版）2025，Vol.46Issue(6)：15-22,8.DOI:10.13705/j.issn.1671-6833.2025.03.010
融合多模态信息的知识感知推荐方法
Knowledge-aware Recommendation Method Integrating Multi-modal Information
摘要
Abstract
It is found that multi-modal information such as images and text possesses semantic complementarity,which could effectively enhance the representation of entities in knowledge graphs,thereby improving the accuracy and interpretability of recommendations.A knowledge-aware recommendation method that could integrate multimo-dal information was proposed by analyzing the characteristics of semantically related multimodal data in recommen-dation systems.On the basis of knowledge graph propagation,multi-modal information that was semantically related to entities in the graph was integrated,and feature fusion was performed with the corresponding entities to enrich entity representation,aiming to explore users' potential interest preferences.In this method,the dependency and interactivity between multimodal information was considered,intermodal attention was adopted to focus on important information of each modality,and semantically associated multimodal embedding features were obtained.Through gated attention,the multi-modal embedding features corresponding to entities were fused with entity representa-tions,further enriching the multi-modal semantic information of entities,thereby enhancing the representation of users and items.In order to verify the effectiveness of the method,experiments were conducted on MovieLens-1M and Book-Crossing data sets,and comparative analysis was conducted with 9 methods including RippletNet,KGAT,CKAN,LKGR,COAT,CKE,KGCN,SKGCR and KGCL.The experimental results showed that it was better than the other two indicators in AUC and ACC.On the MovieLens-1M and Book-Crossing datasets,the AUC of the proposed method were 0.936 6 and 0.763 7,respectively,with an increase of 0.027 2 and 0.029 1 com-pared to the average values of other models.The ACC values of the proposed methods were 0.862 3 and 0.708 9,respectively,with an increase of 0.028 3 and 0.030 5 compared to the average values of other models.关键词
知识图谱/推荐系统/多模态信息/特征融合/嵌入传播Key words
knowledge graph/recommendation system/multi-modal information/feature fusionl/embedding prop-agation分类
信息技术与安全科学引用本文复制引用
王海荣,王怡梦,周北京,易之航..融合多模态信息的知识感知推荐方法[J].郑州大学学报（工学版）,2025,46(6):15-22,8.基金项目
宁夏自然科学基金资助项目(2023AAC03316) （2023AAC03316）
宁夏回族自治区教育厅高等学校科学研究重点项目(NYG2022051) （NYG2022051）
