IMGC-GNN: A multi-granularity coupled graph neural network recommendation method based on implicit relationships
Tóm tắt
In the application recommendation field, collaborative filtering (CF) method is often considered to be one of the most effective methods. As the basis of CF-based recommendation methods, representation learning needs to learn two types of factors: attribute factors revealed by independent individuals (e.g., user attributes, application types) and interaction factors contained in collaborative signals (e.g., interactions influenced by others). However, existing CF-based methods fail to learn these two factors separately; therefore, it is difficult to understand the deeper motivation behind user behaviors, resulting in suboptimal performance. From this point of view, we propose a multi-granularity coupled graph neural network recommendation method based on implicit relationships (IMGC-GNN). Specifically, we introduce contextual information (time and space) into user-application interactions and construct a three-layer coupled graph. Then, the graph neural network approach is used to learn the attribute and interaction factors separately. For attribute representation learning, we decompose the coupled graph into three homogeneous graphs with users, applications, and contexts as nodes. Next, we use multilayer aggregation operations to learn features between users, between contexts, and between applications. For interaction representation learning, we construct a homogeneous graph with user-context-application interactions as nodes. Next, we use node similarity and structural similarity to learn the deep interaction features. Finally, according to the learned representations, IMGC-GNN makes accurate application recommendations to users in different contexts. To verify the validity of the proposed method, we conduct experiments on real-world interaction data from three cities and compare our model with seven baseline methods. The experimental results show that our method has the best performance in the top-k recommendation.
Tài liệu tham khảo
Liang T, Zheng L, Chen L et al (2020) Multi-view factorization machines for mobile app recommendation based on hierarchical attention. Knowl Based Syst 187:104,821
Lei C, Dai H, Yu Z et al (2020) A service recommendation algorithm with the transfer learning based matrix factorization to improve cloud security. Inf Sci 513:98–111
Xue F, He X, Wang X et al (2019) Deep item-based collaborative filtering for top-n recommendation. ACM Trans Inf Syst (TOIS) 37(3):1–25
Liu Y, Yang S, Xu Y et al (2021) Contextualized graph attention network for recommendation with item knowledge graph. IEEE Transactions on knowledge and data engineering
Fan W, Ma Y, Li Q et al (2019) Graph neural networks for social recommendation. In: The world wide web conference, pp 417–426
Harada S, Taniguchi K, Yamada M et al (2019) Context-regularized neural collaborative filtering for game app recommendation. In: RecSys (late-breaking results), pp 16–20
Hao Q, Zhu K, Wang C et al (2022) Cfdil: a context-aware feature deep interaction learning for app recommendation. Soft Comput 26(10):4755–4770
Ebesu T, Shen B, Fang Y (2018) Collaborative memory network for recommendation systems. In: The 41st international ACM SIGIR conference on research & development in informationretrieval, pp 515–524
Yengikand A K, Meghdadi M, Ahmadian S et al (2021) Deep representation learning using multilayer perceptron and stacked autoencoder for recommendation systems. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp 2485–2491
Ahmadian M, Ahmadi M, Ahmadian S et al (2021) Integration of deep sparse autoencoder and particle swarm optimization to develop a recommender system. In: 2021 IEEE International conference on systems, man, and cybernetics (SMC), IEEE, pp 2524–2530
Lin KP, Chang YW, Shen CY et al (2018) Leveraging online word of mouth for personalized app recommendation. IEEE Trans Comput Soc Syst 5(4):1061–1070
Liu Z, Xia X, Lo D et al (2019) Automatic, highly accurate app permission recommendation. Autom Softw Eng 26(2):241– 274
Xu X, Dutta K, Datta A et al (2018) Identifying functional aspects from user reviews for functionality-based mobile app recommendation. J Assoc Inf Sci Technol 69(2):242–255
Sun J, Zhang Y, Guo W et al (2020) Neighbor interaction aware graph convolution networks for recommendation. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in information retrieval, pp 1289–1298
Huang L, Zhao Z L, Wang C D et al (2019) Lscd: Low-rank and sparse cross-domain recommendation. Neurocomputing 366:86–96
Sun J, Zhang Y, Ma C et al (2019) Multi-graph convolution collaborative filtering. In: 2019 IEEE International conference on data mining (ICDM), IEEE, pp 1306–1311
Kumar I, Hu Y, Zhang Y (2022) Eflec: Efficient feature-leakage correction in gnn based recommendation systems. In: Proceedings of the 45th International ACM SIGIR conference on research and development in information retrieval, pp 1885–1889
Duan Z, Wang Y, Ye W et al (2022) Connecting latent relationships over heterogeneous attributed network for recommendation. Applied Intelligence, pp 1–19
Ahmadian M, Ahmadi M, Ahmadian S (2022) A reliable deep representation learning to improve trust-aware recommendation systems. Expert Syst Appl 197:116,697
Wei C, Bai B, Bai K et al (2022) Gsl4rec: Session-based recommendations with collective graph structure learning and next interaction prediction. In: Proceedings of the ACM web conference, vol 2022, pp 2120–2130
Ying R, He R, Chen K et al (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 974–983
Wang X, He X, Wang M et al (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 165–174
Li A, Yang B, Huo H et al (2021) Leveraging implicit relations for recommender systems. Inf Sci 579:55–71
Gao H, Xiao J, Yin Y et al (2022) A mutually supervised graph attention network for few-shot segmentation: The perspective of fully utilizing limited samples. IEEE Transactions on neural networks and learning systems
Gao H, Qiu B, Barroso RJD et al (2022) Tsmae: a novel anomaly detection approach for internet of things time series data using memory-augmented autoencoder. IEEE Transactions on network science and engineering
Guo J, Zhou Y, Zhang P et al (2021) Trust-aware recommendation based on heterogeneous multi-relational graphs fusion. Inf Fusion 74:87–95
Ahmadian S, Ahmadian M, Jalili M (2022) A deep learning based trust-and tag-aware recommender system. Neurocomputing 488:557–571
Xia L, Xu Y, Huang C et al (2021) Graph meta network for multi-behavior recommendation. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 757–766
Fogg BJ (2019) Tiny habits: The small changes that change everything. Eamon Dolan Books
Huskey R, Wilcox S, Weber R (2018) Network neuroscience reveals distinct neuromarkers of flow during media use. J Commun 68(5):872–895
Derfler-Rozin R, Pitesa M (2020) Motivation purity bias: Expression of extrinsic motivation undermines perceived intrinsic motivation and engenders bias in selection decisions. Acad Manag J 63(6):1840–1864
Cai H, Zheng VW, Chang KCC (2018) A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616–1637
Iwana B K, Frinken V, Uchida S (2020) Dtw-nn: a novel neural network for time series recognition using dynamic alignment between inputs and weights. Knowl Based Syst 188:104,971
Zhang S, Yao L, Sun A et al (2019) Deep learning based recommender system: a survey and new perspectives. ACM Computing Surveys (CSUR) 52(1):1–38
Koren Y, Rendle S, Bell R (2022) Advances in collaborative filtering. Recommender systems handbook, pp 91–142
Jiang X, Hu B, Fang Y et al (2020) Multiplex memory network for collaborative filtering. In: Proceedings of the 2020 SIAM international conference on data mining, SIAM, pp 91–99
Tian Z, Liu Y, Sun J et al (2021) Exploiting group information for personalized recommendation with graph neural networks. ACM Trans Inf Syst (TOIS) 40(2):1–23
Guo Z, Yu K, Li Y et al (2021) Deep learning-embedded social internet of things for ambiguity-aware social recommendations. IEEE Transactions on network science and engineering
Yu J, Yin H, Li J et al (2020) Enhance social recommendation with adversarial graph convolutional networks. IEEE Transactions on knowledge and data engineering
Ma Y, Narayanaswamy B, Lin H et al (2020) Temporal-contextual recommendation in real-time. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2291–2299
Herce-Zelaya J, Porcel C, Bernabé-Moreno J et al (2020) New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests. Inf Sci 536:156–170
Hsu CL (2021) A multi-valued and sequential-labeled decision tree method for recommending sequential patterns in cold-start situations. Appl Intell 51(1):506–526