基于元路径引导的多模态推荐算法

doi:10.19678/j.issn.1000-3428.0252046

摘要/Abstract

摘要： 在互联网技术迅猛发展的当下,个性化推荐系统对于帮助用户筛选感兴趣内容扮演着至关重要的角色。传统的推荐方法在处理大规模数据和捕捉用户复杂偏好方面存在局限性,而现有的基于图神经网络(GNN)的推荐方法主要侧重于挖掘用户与物品间的直接交互关系,虽然提高了推荐的准确性,但其往往忽略了如文本、图像、音视频等多模态信息的融合与利用。元路径作为异构图中描述节点间复合关系的概念,有助于进一步提升嵌入质量和推荐效果,但现有模型要么忽略节点内容特征,要么丢弃了元路径上的中间节点,或者仅考虑单一的元路径。针对现有多模态推荐系统的挑战,提出了一种基于元路径引导的多模态推荐方法MAMGNN。首先通过构建多模态异构图,整合来自不同模态的信息,然后利用元路径来引导信息在同种元路径内及不同种元路径间进行传播和聚合。此外,该方法还引入了GNN和注意力机制,以学习用户和物品的高质量嵌入表示,从而生成更精确且具有可解释性的推荐结果。在MovieLens-20M和H&M两个真实世界数据集上的广泛实验及小范围内的用户调研,实验结果表明,MAMGNN在预测用户对项目的偏好程度方面效果显著提升,相较于次优模型,在MovieLens-20M数据集上的Precision@10、Recall@10和NDCG@10分别提高了2.93%、1.98%、2.12%,在H&M数据集上分别提高了3.43%、1.18%、2.40%。

关键词: 多模态推荐, 元路径, 异构图, 图神经网络, 可解释推荐

Abstract: With the rapid development of Internet technology, personalized recommendation systems have become pivotal in helping users filter content of interest. Traditional recommendation methods have limitations in processing large-scale data and capturing the complex preferences of users. Existing recommendation methods based on Graph Neural Networks (GNNs) primarily focus on mining direct interactions between users and items. Although they improve the accuracy of recommendations, they often ignore the integration and utilization of multimodal information such as text, images, audio, and videos. Metapath, which is a concept that describes the composite relationship between nodes in heterogeneous graphs, can further improve the embedding quality and recommendation effect. However, the existing models either ignore node content features, discard intermediate nodes on the metapath, or consider only a single metapath. To address the challenges of existing multimodal recommendation systems, this study proposes a multimodal recommendation algorithm based on metapath guidance MAMGNN. First, it constructs a multimodal heterogeneous information network to integrate information from different modalities and then uses metapaths to guide the propagation and aggregation of information within the intra-metapath and inter-metapath. Furthermore, it introduces GNNs and attention mechanisms to learn high-quality embedding representations of users and items, generating more accurate and explainable recommendation results. Extensive experiments on two real-world datasets, namely, MovieLens-20M and H&M, and a small-scale user survey demonstrate that the MAMGNN significantly enhances the performance in predicting user preferences for items, outperforming suboptimal models in the Precision@10, Recall@10, and NDCG@10 metrics by approximately 2.93%, 1.98%, 2.12%, and 3.43%, 1.18%, and 2.40%, respectively.

Key words: multimodal recommendation, metapath, heterogeneous graph, Graph Neural Network (GNN), explainable recommendation

中图分类号:

TP391

石旭, 解庆, 汤梦姿, 王玉菡, 刘永坚. 基于元路径引导的多模态推荐算法[J]. 计算机工程, 2026, 52(10): 285-301.

SHI Xu, XIE Qing, TANG Mengzi, WANG Yuhan, LIU Yongjian. Multimodal Recommendation Algorithm Based on Metapath Guidance[J]. Computer Engineering, 2026, 52(10): 285-301.

https://www.ecice06.com/CN/Y2026/V52/I10/285

参考文献

[1] RAZA S, RAHMAN M, KAMAWAL S, et al. A comprehensive review of recommender systems: transitioning from theory to practice[EB/OL].[2024-12-10]. https://arxiv.org/pdf/2407.13699.
[2] KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30-37.
[3] HE X N, LIAO L Z, ZHANG H W, et al. Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web. New York, USA: ACM Press, 2017: 173-182.
[4] YING R, HE R N, CHEN K F, et al. Graph convolutional neural networks for web-scale recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: ACM Press, 2018: 964-955.
[5] WU S W, SUN F, ZHANG W T, et al. Graph neural networks in recommender systems: a survey[J]. ACM Computing Surveys, 2022, 55(5): 1-37.
[6] WANG X, HE X N, WANG M, et al. Neural graph collaborative filtering[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2019: 165-174.
[7] DONG Y X, CHAWLA N V, SWAMI A. metapath2vec: scalable representation learning for heterogeneous networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2017: 135-144.
[8] SHI C, HU B B, ZHAO W X, et al. Heterogeneous information network embedding for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 31(2): 357-370.
[9] FU T Y, LEE W C, LEI Z. HIN2Vec: explore meta-paths in heterogeneous information networks for representation learning[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. New York, USA: ACM Press, 2017: 1797-1806.
[10] WANG X, JI H Y, SHI C, et al. Heterogeneous graph attention network[C]//Proceedings of The World Wide Web Conference. New York, USA: ACM Press, 2019: 2022-2032.
[11] DELDJOO Y, SCHEDL M, CREMONESI P, et al. Recommender systems leveraging multimedia content[J]. ACM Computing Surveys, 2020, 53(5): 1-38.
[12] ZHU J M, DAI Q Y, SU L C, et al. BARS: towards open benchmarking for recommender systems[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2022: 2912-2923.
[13] YUAN Z, YUAN F J, SONG Y, et al. Where to go next for recommender systems? ID-vs. modality-based recommender models revisited[EB/OL].[2024-12-10]. https://arxiv.org/pdf/2303.13835.
[14] NOJAVANASGHARI B, GOPINATH D, KOUSHIK J, et al. Deep multimodal fusion for persuasiveness prediction[C]//Proceedings of the 18th ACM International Conference on Multimodal Interaction. New York, USA: ACM Press, 2016: 284-288.
[15] PÉREZ-RA J M, VIELZEUF V, PATEUX S, et al. MFAS: multimodal fusion architecture search[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE Press, 2019: 6966-6975.
[16] WEI Y W, WANG X, NIE L Q, et al. MMGCN: multi-modal graph convolution network for personalized recommendation of micro-video[C]//Proceedings of the 27th ACM International Conference on Multimedia. New York, USA: ACM Press, 2019: 1437-1445.
[17] TAO Z L, WEI Y W, WANG X, et al. MGAT: multimodal graph attention network for recommendation[J]. Information Processing & Management, 2020, 57(5): 102277.
[18] LIU S, CHEN Z Z, LIU H Y, et al. User-video co-attention network for personalized micro-video recommendation[C]//Proceedings of The World Wide Web Conference. New York, USA: ACM Press, 2019: 3020-3026.
[19] 王紫萱,张凯涵,蔡江辉,等.融合多模态特征的可解释推荐算法[J].计算机系统应用,2025,34(3):62-71. WANG Z X, ZHANG K H, CAI J H, et al. Explainable recommendation algorithm fusing multimodal features[J]. Computer Systems Applications,2025,34(3):62-71. (in Chinese)
[20] CHEN W, HUANG P P, XU J M, et al. POG: personalized outfit generation for fashion recommendation at Alibaba iFashion[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: ACM Press, 2019: 2662-2670.
[21] WU C H, WU F Z, QI T, et al. MM-Rec: visiolinguistic model empowered multimodal news recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press,2022: 2560-2564.
[22] LIU Y, YANG S S, LEI C Y, et al. Pre-training graph transformer with multimodal side information for recommendation[C]//Proceedings of the 29th ACM International Conference on Multimedia. New York, USA: ACM Press, 2021: 2853-2861.
[23] SUN R, CAO X Z, ZHAO Y, et al. Multi-modal knowledge graphs for recommender systems[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management. New York, USA: ACM Press, 2020: 1405-1414.
[24] REN X, XIA L, ZHAO J, et al. Disentangled contrastive collaborative filtering[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2023: 1137-1146.
[25] LIU K, XUE F, GUO D, et al. Multimodal graph contrastive learning for multimedia-based recommendation[J]. IEEE Transactions on Multimedia, 2023, 25: 9343-9355.
[26] WEI W, HUANG C, XIA L H, et al. Multi-modal self-supervised learning for recommendation[C]//Proceedings of the ACM Web Conference. New York, USA: ACM Press, 2023: 790-800.
[27] SUN Y, HAN J, YAN X, et al. PathSim: meta path-based top-k similarity search in heterogeneous information networks[J]. Proceedings of the VLDB Endowment, 2011, 4(11): 992-1003.
[28] SHI C, KONG X N, HUANG Y, et al. HeteSim: a general framework for relevance measure in heterogeneous networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(10): 2479-2492.
[29] YU X, REN X, SUN Y Z, et al. Personalized entity recommendation: a heterogeneous information network approach[C]//Proceedings of the 7th ACM International Conference on Web Search and Data Mining. New York, USA: ACM Press, 2014: 283-292.
[30] HUANG Z P, ZHENG Y D, CHENG R, et al. Meta structure: computing relevance in large heterogeneous information networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2016: 1595-1604.
[31] ZHAO H, YAO Q M, LI J D, et al. Meta-graph based recommendation fusion over heterogeneous information networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2017: 635-644.
[32] 李亚聪,刘皓冰,蒋若冰,等.异质图表征学习综述[J].软件学报, 2025, 36(6): 2794-2826. LI Y C, LIU H B, JIANG R B, et al. Survey on heterogeneous graph representation learning[J]. Journal of Software, 2025, 36(6): 2794-2826. (in Chinese)
[33] 漆盛, 高榕,邵雄凯,等.面向超图的可解释性对比元路径群组推荐[J].计算机工程与应用,2024,60(11):268-280. QI S, GAO R, SHAO X K, et al. Hypergraph-based meta-path explanation contrastive learning for group recommendation[J]. Computer Engineering and Applications, 2024, 60(11): 268-280. (in Chinese)
[34] LI Q, TIAN Y, WANG X Y, et al. MetapathVis: inspecting the effect of metapath in heterogeneous network embedding via visual analytics[J]. Computer Graphics Forum, 2025, 44(1): e15285.
[35] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[EB/OL].[2024-12-10].https://arxiv.org/pdf/1502.03167.
[36] 刘仲民, 杨富君, 胡文瑾. 多尺度特征交互的伪标签无监督域自适应行人重识别[J]. 光电工程, 2025, 52(1): 240238. LIU Z M, YANG F J, HU W J. Multi-scale feature interaction pseudo-label unsupervised domain adaptation for person re-identification[J]. Opto-Electronic Engineering, 2025, 52(1): 240238. (in Chinese)
[37] GroupLens Research. MovieLens-20M dataset[EB/OL]. [2024-12-10]. https://grouplens.org/datasets/movielens/20m/.
[38] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE Press, 2016: 770-778.
[39] DEVLIN J, CHANG M-W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of Conference on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis, USA: Association for Computational Linguistics, 2019: 4171-4186.
[40] Kaggle. H&M personalized fashion recommendations[EB/OL]. [2024-12-10]. https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/data.
[41] CEN Y K, ZOU X, ZHANG J W, et al. Representation learning for attributed multiplex heterogeneous network[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: ACM Press, 2019: 1358-1368.
[42] TAO Z L, LIU X H, XIA Y W, et al. Self-supervised learning for multimedia recommendation[J]. IEEE Transactions on Multimedia, 2022, 25: 5107-5116.
[43] YANG C, LIU M X, HE F, et al. Similarity modeling on heterogeneous networks via automatic path discovery[C]//Proceedings of Machine Learning and Knowledge Discovery in Databases: European Conference. Berlin, Germany: Springer, 2019: 37-54.
[44] CHEN C, ZHANG M, LIU Y, et al. Neural attentional rating regression with review-level explanations[C]//Proceedings of the 2018 World Wide Web Conference. New York, USA: ACM Press, 2018: 1583-1592.
[45] WANG X T, CHEN Y R, YANG J, et al. A reinforcement learning framework for explainable recommendation[C]//Proceedings of IEEE International Conference on Data Mining. Washington D.C., USA: IEEE Press, 2018: 587-596.

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