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Review Article

Weighted meta-graph based mobile application recommendation through matrix factorisation and neural networks

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Article: 2289834 | Received 09 Sep 2023, Accepted 27 Nov 2023, Published online: 30 Dec 2023

References

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.
  • Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. In Proceedings of compstat'2010 (pp. 177–186). Springer.
  • Cai, J., Liang, W., Li, X., Li, K., Gui, Z., & Khan, M. K (2023, December 15). GTxChain: A secure ioT smart blockchain architecture based on graph neural network. IEEE Internet of Things Journal, 10(24).
  • Chen, J., Gong, Z., Li, Y., Zhang, H., Yu, H., Zhu, J., Fan, G, Wu, X.-M., & Wu, K. (2022). Meta-path based neighbors for behavioral target generalization in sequential recommendation. IEEE Transactions on Network Science and Engineering, 9(3), 1658–1667. https://doi.org/10.1109/TNSE.2022.3149328
  • Chen, J., Liu, Y., Zhao, S., & Zhang, Y. (2019). Citation recommendation based on weighted heterogeneous information network containing semantic linking. In Proceedings of the IEEE international conference on multimedia and expo (pp. 31–36). IEEE.
  • Coelho, J., Mano, D., Paula, B., Coutinho, C., Oliveira, J., Ribeiro, R., & Batista, F. (2023). Semantic similarity for mobile application recommendation under scarce user data. Engineering Applications of Artificial Intelligence, 121, Article 105974. https://doi.org/10.1016/j.engappai.2023.105974
  • Fan, S., Zhu, J., Han, X., Shi, C., Hu, L., Ma, B., & Li, Y. (2019). Metapath-guided heterogeneous graph neural network for intent recommendation. In Proceedings of the 25th ACM sigkdd international conference on knowledge discovery & data mining (pp. 2478–2486). ACM.
  • Fang, Y., Lin, W., Zheng, V. W., Wu, M., Chang, K. C., & Li, X. (2016). Semantic proximity search on graphs with metagraph-based learning. In Proceedings of the IEEE international conference on data engineering (pp. 277–288). IEEE.
  • Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics (Vol 15, pp. 315–323). PMLR.
  • Gong, W., Zhang, W., Bilal, M., Chen, Y., Xu, X., & Wang, W. (2022). Efficient web APIs recommendation with privacy-Preservation for Mobile app development in Industry 4.0. IEEE Transactions on Industrial Informatics, 18(9), 6379–6387. https://doi.org/10.1109/TII.2021.3133614
  • He, X., & Chua, T. (2017). Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval (pp. 355–364). ACM.
  • Huang, C., Fang, Y., Lin, X., Cao, X., & Zhang, W. (2022). ABLE: Meta-path prediction in heterogeneous information networks. ACM Transactions on Knowledge Discovery From Data, 16(4), 73:1–73:21. https://doi.org/10.1145/3494558
  • Huang, Z., Zheng, Y., Cheng, R., Sun, Y., Mamoulis, N., & Li, X. (2016). Meta structure: Computing relevance in large heterogeneous information networks. In Proceedings of the 22nd ACM sigkdd international conference on knowledge discovery and data mining (pp. 1595–1604). ACM.
  • Jozani, M. M., Liu, C. Z., & Choo, K. R. (2023). An empirical study of content-based recommendation systems in mobile app markets. Decision Support Systems, 169, Aritcle 113954. https://doi.org/10.1016/j.dss.2023.113954
  • Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. In Proceedings of the annual conference on neural information processing systems (pp. 556–562). MIT Press.
  • Li, Y., Liang, W., Xie, K., Zhang, D., Xie, S., & Li, K. (2023). LightNestle: Quick and accurate neural sequential tensor completion via meta learning. In IEEE conference on computer communications (infocom) (pp. 1–10). IEEE.
  • Lian, S., & Tang, M. (2022). API recommendation for Mashup creation based on neural graph collaborative filtering. Connection Science, 34(1), 124–138. https://doi.org/10.1080/09540091.2021.1974819
  • Liang, T., He, L., Lu, C., Chen, L., Yu, P. S., & Wu, J. (2017). A broad learning approach for context-aware mobile application recommendation. In Proceedings of the IEEE international conference on data mining (pp. 955–960). IEEE Computer Society.
  • Liang, T., Sheng, X., Zhou, L., Li, Y., Gao, H., Yin, Y., & Chen, L. (2021). Mobile app recommendation via heterogeneous graph neural network in edge computing. Applied Soft Computing, 103, Article 107162. https://doi.org/10.1016/j.asoc.2021.107162
  • Liang, T., Zheng, L., Chen, L., Wan, Y., Philip, S. Y., & Wu, J. (2020). Multi-view factorization machines for Mobile app recommendation based on hierarchical attention. Knowledge-Based Systems, 187, Article 104821. https://doi.org/10.1016/j.knosys.2019.06.029
  • Liang, W., Li, Y., Xie, K., Zhang, D., Li, K. C., Souri, A., & Li, K (2023, August). Spatial-temporal aware inductive graph neural network for C-ITS data recovery. IEEE Transactions on Intelligent Transportation Systems, 24(8), 8431–8442.
  • Liang, W., Yang, Y., Yang, C., Hu, Y., Xie, S., Li, K. C., & Cao, J (2023, June). PDPChain: A consortium blockchain-based privacy protection scheme for personal data. IEEE Transactions on Reliability, 72(2), 586–598.
  • Long, J., Liang, W., Li, K. C., Wei, Y., & Marino, M. D. (2022). A regularized cross-layer ladder network for intrusion detection in industrial internet of things. IEEE Transactions on Industrial Informatics, 19(2), 1747–1755. https://doi.org/10.1109/TII.2022.3204034
  • Maqbool, M. H., Farooq, U., Mosharrof, A., Siddique, A. B., & Foroosh, H. (2023). MobileRec: A large scale dataset for mobile apps recommendation. In Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval (pp. 3007–3016). ACM.
  • Mnih, A., & Salakhutdinov, R. R. (2008). Probabilistic matrix factorization. In Proceedings of the annual conference on neural information processing systems (pp. 1257–1264). Curran Associates, Inc.
  • Ouyang, Y., Guo, B., Wang, Q., Liang, Y., & Yu, Z. (2023). Learning dynamic app usage graph for next Mobile app recommendation. IEEE Transactions on Mobile Computing, 22(8), 4742–4753. https://doi.org/10.1109/TMC.2022.3161114
  • Peng, M., Cao, B., Chen, J., Liu, J., & Hu, R. (2021). MR-FI: Mobile application recommendation based on feature importance and bilinear feature interaction. In Proceedings of the 17th EAI international conference on collaborative computing: Networking, applications and worksharing (Vol 406, pp. 213–228). Springer.
  • Peng, M., Zeng, G., Sun, Z., Huang, J., Wang, H., & Tian, G. (2018). Personalized app recommendation based on app permissions. World Wide Web, 21(1), 89–104. https://doi.org/10.1007/s11280-017-0456-y
  • Rendle, S. (2010). Factorization machines. In Proceedings of the international conference on data mining (pp. 995–1000). IEEE.
  • Shi, C., Li, Y., Zhang, J., Sun, Y., & Yu, P. S. (2017). A survey of heterogeneous information network analysis. IEEE Transactions on Knowledge and Data Engineering, 29(1), 17–37. https://doi.org/10.1109/TKDE.2016.2598561
  • Shi, C., Zhang, Z., Ji, Y., Wang, W., Philip, S. Y., & Shi, Z. (2019). SemRec: A personalized semantic recommendation method based on weighted heterogeneous information networks. World Wide Web, 22(1), 153–184. https://doi.org/10.1007/s11280-018-0553-6
  • Sun, Y., Han, J., Yan, X., Yu, P. S., & Wu, T. (2011). Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment, 4(11), 992–1003. https://doi.org/10.14778/3402707.3402736
  • Tang, M., Tang, W., & Xie, F (2023, November). Accurately predicting quality of services in ioT via using self-attention representation and deep factorization machines. IEEE Transactions on Intelligent Transportation Systems, 24(11), 13276–13285.
  • Tu, Z., Li, Y., Hui, P., Su, L., & Jin, D. (2020, November 1). Personalized Mobile app prediction by learning user's interest from social media. IEEE Transactions on Mobile Computing, 19(11), 2670–2683.
  • Wang, R., Ma, X., Jiang, C., Ye, Y., & Zhang, Y. (2020). Heterogeneous information network-based music recommendation system in mobile networks. Computer Communications, 150, 429–437. https://doi.org/10.1016/j.comcom.2019.12.002
  • Wang, X., Jia, L., Guo, L., & Liu, F. (2023). Multi-aspect heterogeneous information network for MOOC knowledge concept recommendation. Applied Intelligence, 53(10), 11951–11965. https://doi.org/10.1007/s10489-022-04025-x
  • Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https://doi.org/10.3354/cr030079
  • Xie, F., Cao, Z., Xu, Y., Chen, L., & Zheng, Z. (2020). Graph neural network and multi-view learning based mobile application recommendation in heterogeneous graphs. In Proceedings of the IEEE international conference on services computing (pp. 100–107). IEEE.
  • Xie, F., Chen, L., Ye, Y., Liu, Y., Zheng, Z., & Lin, X. (2018). A weighted meta-graph based approach for mobile application recommendation on heterogeneous information networks. In Proceedings of the international conference on service-oriented computing (pp. 404–420). Springer.
  • Xie, F., Zheng, A., Chen, L., & Zheng, Z. (2021). Attentive meta-graph embedding for item recommendation in heterogeneous information networks. Knowledge-Based Systems, 211, Article 106524. https://doi.org/10.1016/j.knosys.2020.106524
  • Xiong, X., Qiao, S., Han, N., Xiong, F., Bu, Z., Li, R. H., Yue, K., & Yuan, G. (2020). Where to go: An effective point-of-interest recommendation framework for heterogeneous social networks. Neurocomputing, 373, 56–69. https://doi.org/10.1016/j.neucom.2019.09.060
  • Xu, Y., Zhou, D., & Ma, J. (2019). Scholar-friend recommendation in online academic communities: An approach based on heterogeneous network. Decision Support Systems, 119, 1–13. https://doi.org/10.1016/j.dss.2019.01.004
  • Yao, Y., Zhao, W. X., Wang, Y., Tong, H., Xu, F., & Lu, J. (2017). Version-aware rating prediction for mobile app recommendation. ACM Transactions on Information Systems, 35(4), 38:1–38:33. https://doi.org/10.1145/3015458
  • Yin, H., Wang, W., Chen, L., Du, X., Nguyen, Q. V. H., & Huang, Z. (2018). Mobi-SAGE-RS: A sparse additive generative model-based Mobile application recommender system. Knowledge-Based Systems, 157, 68–80. https://doi.org/10.1016/j.knosys.2018.05.028
  • Yu, J., Gao, M., Li, J., Yin, H., & Liu, H. (2018). Adaptive implicit friends identification over heterogeneous network for social recommendation. In Proceedings of the 27th ACM international conference on information and knowledge management (pp. 357–366). ACM.
  • Zhao, C., Wang, H., Li, Y., & Mu, K. (2019). Combining meta-graph and attention for recommendation over heterogenous information network. In Proceedings of the international conference on database systems for advanced applications (pp. 383–397). Springer.
  • Zhao, H., Yao, Q., Li, J., Song, Y., & Lee, D. L. (2017). Meta-graph based recommendation fusion over heterogeneous information networks. In Proceedings of the 23rd ACM sigkdd international conference on knowledge discovery and data mining (pp. 635–644). ACM.
  • Zhu, K., Zhang, L., & Pattavina, A. (2017). Learning geographical and Mobility factors for mobile application recommendation. IEEE Intelligent Systems, 32(3), 36–44. https://doi.org/10.1109/MIS.2017.52