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Multiple Biases-Incorporated Latent Factorization of Tensors

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Dynamic Network Representation Based on Latent Factorization of Tensors

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

An HDI tensor modeling a dynamic network contains rich knowledge regarding dynamic network evolution. An LFT-based model has attracted plenty of attention on extracting useful knowledge form an HDI tensor. However, existing LFT-based models lack solid consideration for the volatility of dynamic network data, thereby leading to the descent of model representation learning ability. To tackle this problem, this chapter proposes a multiple biases-incorporated latent factorization of tensors (MBLFT) model, which incorporates preprocessing bias, short-term bias and long-term bias into an LFT-based model. Empirical studies on two large-scale dynamic networks generated by industrial applications show that the proposed MBLFT model achieves higher prediction accuracy than state-of-the-art models in solving missing link prediction task.

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References

  1. Hu, L., Zhang, J., Pan, X., Luo, X., Yuan, H.: An effective link-based clustering algorithm for detecting overlapping protein complexes in protein-protein interaction networks. IEEE Trans. Netw. Sci. Eng. 8(4), 3275–3289 (2021)

    Article  Google Scholar 

  2. Shahriar, N., Ahmed, R., Chowdhury, S.R., Khan, M.M.A., Boutaba, R., Mitra, J., Zeng, F.: Virtual network embedding with guaranteed connectivity under multiple substrate link failures. IEEE Trans. Commun. 68(2), 1025–1043 (2020)

    Article  Google Scholar 

  3. Song, Y., Li, M., Luo, X., Yang, G., Wang, C.: Improved symmetric and nonnegative matrix factorization models for undirected, sparse and large-scaled networks: a triple factorization-based approach. IEEE Trans. Ind. Info. 16(5), 3006–3017 (2020)

    Article  Google Scholar 

  4. Luo, X., Zhou, M.C., Li, S., Shang, M.: Algorithms of unconstrained non-negative latent factor analysis for recommender systems. IEEE Trans. Big Data. 7(1), 227–240 (2021)

    Article  Google Scholar 

  5. Zhang, W., Sun, H., Liu, X., Guo, X.: Temporal QoS-aware web service recommendation via non-negative tensor factorization. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 585–596, Seoul, ROK (2014)

    Google Scholar 

  6. Luo, X., Zhou, M.C., Li, S., You, Z., Xia, Y., Zhu, Q.: A non-negative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method. IEEE Trans. Neural Netw. Learn. Syst. 27(3), 524–537 (2016)

    Article  MathSciNet  Google Scholar 

  7. Martínez, V., Berzal, F., Cubero, J.C.: A survey of link prediction in complex networks. ACM Comput. Surv. 49(4), 1–33 (2016)

    Article  Google Scholar 

  8. Yang, L., Cao, X., Jin, D., Wang, X., Meng, D.: A unified semi-supervised community detection framework using latent space graph regularization. IEEE Trans. Cybern. 45(11), 2585–2598 (2015)

    Article  Google Scholar 

  9. Luo, X., Zhou, M.C., Xia, Y., Zhu, Q., Ammari, A.C., Alabdulwahab, A.: Generating highly accurate predictions for missing QoS-data via aggregating non-negative latent factor models. IEEE Trans. Neural Netw. Learn. Syst. 27(3), 579–592 (2016)

    Article  MathSciNet  Google Scholar 

  10. Luan, W.J., Liu, G.J., Jiang, C.J., Qi, L.: Partition-based collaborative tensor factorization for POI recommendation. IEEE/CAA J. Automatica Sinica. 4(3), 437–446 (2017)

    Article  MathSciNet  Google Scholar 

  11. Ning, Z., Cheung, W.K., Guoping, Q., Xiangyang, X.: A hybrid probabilistic model for unified collaborative and content-based image tagging. IEEE Trans. Pattern Anal. Mach. Intell. 33(7), 1281–1294 (2011)

    Article  Google Scholar 

  12. Cheng, D., Huang, J., Zhang, S., Zhang, X., Luo, X.: A novel approximate spectral clustering algorithm with dense cores and density peaks. IEEE Trans. Syst. Man Cybern. Syst. 52(4), 2348–2360 (2022)

    Article  Google Scholar 

  13. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)

    Article  Google Scholar 

  14. Xie, Z., Jin, L., Luo, X., Bin, H., Li, S.: An acceleration-level data-driven repetitive motion planning scheme for kinematic control of robots with unknown structure. IEEE Trans. Syst. Man Cybern. Syst. 52(9), 5679–5691 (2022)

    Article  Google Scholar 

  15. Y, K., R, B., C, V.: Matrix-factorization techniques for recommender systems. IEEE Computer. 42, 30–37 (2009)

    Article  Google Scholar 

  16. Yuan, Y., Luo, X., Shang, M.: Effects of preprocessing and training biases in latent factor models for recommender systems. Neurocomputing. 275, 2019–2030 (2018)

    Article  Google Scholar 

  17. Chen, J., Luo, X., Zhou, M.C.: Hierarchical particle swarm optimization-incorporated latent factor analysis for large-scale incomplete matrices. IEEE Trans. Big Data. https://doi.org/10.1109/TBDATA.2021.3090905

  18. Salakhutdinov, R., Mnih, A.: Probabilistic Matrix-factorization. Adv. Neural Inf. Proces. Syst. 20, 1257–1264 (2008)

    Google Scholar 

  19. Yu, K., Zhu, S., Lafferty, J., Gong, Y.: Fast nonparametric matrix-factorization for large-scale collaborative-filtering. In: Proceedings of the 32nd ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 211–218, Boston, MA, USA (2009)

    Google Scholar 

  20. Luo, X., Liu, Z., Jin, L., Zhou, Y., Zhou, M.C.: Symmetric non-negative matrix factorization-based community detection models and their convergence analysis. IEEE Trans. Neural Netw. Learn. Syst. 33(3), 1203–1215 (2022)

    Article  MathSciNet  Google Scholar 

  21. Koren, Y.: Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 447–456, Paris, France (2009)

    Google Scholar 

  22. Sahoo, N., Singh, P., Mukhopadhyay, T.: A hidden Markov model for collaborative filtering. MIS Quarterly. 36(4), 1329–1356 (2012)

    Article  Google Scholar 

  23. Wang, S., Ma, Y., Cheng, B., Yang, F., Chang, R.N.: Multi-dimensional QoS prediction for service recommendations. IEEE Trans. Serv. Comput. 12(1), 47–57 (2016)

    Article  Google Scholar 

  24. Luo, X., Wu, H., Wang, Z., Wang, J., Meng, D.: A novel approach to large-scale dynamically weighted directed network representation. IEEE Trans. Pattern Anal. Mach. Intell. https://doi.org/10.1109/TPAMI.2021.3132503

  25. Acar, E., Dunlavy, D.M., Kolda, T.G., Morup, M.: Scalable tensor factorizations with missing data. In: Proceedings SIAM International Conference on Data Mining, Columbus, USA, pp. 701–712 (2010)

    Google Scholar 

  26. Luo, X., Zhou, M.C., Wang, Z., Xia, Y., Zhu, Q.: An effective QoS estimating scheme via alternating direction method-based matrix factorization. IEEE Trans. Serv. Comput. 12(4), 503–518 (2019)

    Article  Google Scholar 

  27. Wu, Y.K., Tan, H.C., Li, Y., Zhang, J., Chen, X.X.: A fused CP factorization method for incomplete tensors. IEEE Trans. Neural Netw. Learn. Syst. 30(3), 751–764 (2019)

    Article  Google Scholar 

  28. Luo, X., Zhou, M.C., Li, S., You, Z., Xia, Y., Zhu, Q., Leung, H.: An efficient second-order approach to factorizing sparse matrices in recommender systems. IEEE Trans. Industr. Inform. 11(4), 946–956 (2015)

    Article  Google Scholar 

  29. Papalexakis, E., Faloutsos, C., Sidiropoulos, N.: Tensors for data mining and data fusion: models, applications, and scalable algorithms. ACM Trans. Intell. Syst. Technol. 8(2), 1–44 (2017)

    Article  Google Scholar 

  30. Ming, X., Jianping, W., Wang, H., Cao, M.: Anomaly detection in road networks using sliding-window tensor factorization. IEEE Trans. Intell. Transp. Syst. 20(12), 4704–4713 (2019)

    Article  Google Scholar 

  31. Chen, J., Xin Luo, Y., Shang, M.: Performance of latent factor models with extended linear biases. Knowl.-Based Syst. 123(1), 128–136 (2017)

    Article  Google Scholar 

  32. Li, H., Zhang, Q.F., Deng, J.D.: Biased multiobjective optimization and decomposition algorithm. IEEE Trans. Cybern. 47(1), 52–66 (2017)

    Article  Google Scholar 

  33. Luo, X., Hao, W., Zhou, M.C., Yuan, H.: Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors. IEEE Trans. Cybern. 50(5), 1798–1809 (2020)

    Article  Google Scholar 

  34. Wu, D., Shang, M., Luo, X., Wang, Z.: An L1-and-L2-norm-oriented latent factor model for recommender systems. IEEE Trans. Neural Netw. Learn. Syst. https://doi.org/10.1109/TNNLS.2021.3071392

  35. Luo, X., Zhou, M.C., Li, S., Shang, M.: An inherently non-negative latent factor model for high-dimensional and sparse matrices from industrial applications. IEEE Trans. Industr. Inform. 14(5), 2011–2022 (2018)

    Article  Google Scholar 

  36. Takács, G., Pilászy, I., Németh, B., Tikky, D.: Scalable collaborative filtering approaches for large recommender systems. J. Mach. Learn. Res. 10, 623–656 (2009)

    Google Scholar 

  37. Yuan, Y., Luo, X., Shang, M., Di, W.: A Generalized and Fast-Converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems. In: Proceedings of the 29th International Conference on World Wide Web 2020, pp. 498–507

    Google Scholar 

  38. Luo, X., Zhou, M.C., Li, S., Lun, H., Shang, M.: Non-negativity constrained missing data estimation for high-dimensional and sparse matrices from industrial applications. IEEE Trans. Cybern. 50(5), 1844–1855 (2020)

    Article  Google Scholar 

  39. Wu, H., Luo, X.: Instance-frequency-weighted regularized, nonnegative and adaptive latent factorization of tensors for dynamic QoS analysis. In: Proceedings of the 2021 IEEE International Conference on Web Services. (ICWS2021), pp. 560–568 (2021)

    Chapter  Google Scholar 

  40. Wu, X., Wang, J., Wu, H.: Multiple biases-incorporated latent factorization of tensors for dynamic network link prediction. In: Proceedings of the 2020 IEEE International Conference on Innovation in Artificial Intelligence. (ICIAI 2020), pp. 36–41

    Google Scholar 

  41. Wu, X., Gou, H., Wu, H., Wang, J., Chen, M.: A diverse biases non-negative latent factorization of tensors model for dynamic network link prediction. In: Proceedings of the 2020 IEEE International Conference on Networking, Sensing and Control. (ICNSC 2020). https://doi.org/10.1109/ICNSC48988.2020.9238117

  42. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Neveda, pp. 426–434 (2008)

    Chapter  Google Scholar 

  43. Hao, W., Luo, X., Zhou, M.C., Rawa, M.J., Sedraoui, K., Albeshri, A.: A PID-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis. IEEE/CAA J. Automatica Sinica. 9(3), 533–546 (2022)

    Article  Google Scholar 

  44. Zhang, Y., Zheng, Z., Lyu, M.R.: WSPred: a time-aware personalized QoS prediction framework for web services. In: Proceedings of the IEEE 22nd International Symposium on Software Reliability Engineering, Hiroshima, JPN, pp. 210–219 (2011)

    Google Scholar 

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Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, China

    Hao Wu & Xin Luo

  2. Chongqing Institute of Green and Intelli, Chongqing, China

    Xuke Wu

Authors
  1. Hao Wu
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  2. Xuke Wu
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  3. Xin Luo
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Wu, H., Wu, X., Luo, X. (2023). Multiple Biases-Incorporated Latent Factorization of Tensors. In: Dynamic Network Representation Based on Latent Factorization of Tensors. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-19-8934-6_2

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  • DOI: https://doi.org/10.1007/978-981-19-8934-6_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8933-9

  • Online ISBN: 978-981-19-8934-6

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