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Personalized tag recommendation via denoising auto-encoder

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Abstract

Personalized tag recommender systems automatically recommend users a set of tags used to annotate items according to users’ past tagging information. Learning the representations of involved entities (i.e. users, items and tags) and capturing the complex relationships among them are crucial for personalized tag recommender systems. However, few studies have been conducted to simultaneously achieve these two sub-goals. In this research, we propose a novel personalized tag recommendation model based on the denoising auto-encoder, namely DAE-PTR, which learns the representations of entities and encodes the complex relationships by exploiting the denoising auto-encoder framework. Specifically, for each user, we firstly generate the corrupted version of the respective tagging information by adding the multiplicative mask-out/drop-out noise into the original input. Then, we learn the latent representations from the corrupted input via the auto-encoder framework by using the cross-entropy loss. More importantly, we integrate the latent user and item embeddings into the processing of encoding, which makes the learnt hidden representations of the auto-encoder network encode multiple types of relationships among entities, i.e. the relationships between users and tags, between items and tags, and among tags. Finally, we employ the decoder component to reconstruct the original input based on the learnt latent representations. Experimental results on the real-world datasets show that our proposed DAE-PTR model is superior to the traditional personalized tag recommendation models.

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Notes

  1. Two datasets can be found in https://grouplens.org/datasets/hetrec-2011/

References

  1. 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(6), 734–749 (2005)

    Article  Google Scholar 

  2. Askari, B., Szlichta, J., Salehi-Abari, A.: Variational autoencoders for top-k recommendation with implicit feedback. In: SIGIR, pp. 2061–2065 (2021)

  3. Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)

    Article  Google Scholar 

  4. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: NIPS, pp. 153–160 (2006)

  5. Breese, J. S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI, pp. 43–52 (1998)

  6. Cai, T., Li, J., Mian, A. S., Sellis, T., Yu, J. X., et al.: Target-aware holistic influence maximization in spatial social networks. In: IEEE Transactions on Knowledge and Data Engineering (2020)

  7. Cai, Y., Zhang, M., Luo, D., Ding, C., Chakravarthy, S.: Low-order tensor decompositions for social tagging recommendation. In: WSDM, pp. 695–704 (2011)

  8. Chandar, A. P. S., Lauly, S., Larochelle, H., Khapra, M. M., Ravindran, B., Raykar, V., Saha, A.: An autoencoder approach to learning bilingual word representations. In: NIPS, pp. 1853–1861 (2014)

  9. Chapelle, O., Scholkopf, B., Zien, E.A: Semi-supervised learning (chapelle, o. others, eds.; 2006) [book reviews]. IEEE Trans on Neural Netw 20(3), 542–542 (2009)

    Article  Google Scholar 

  10. Chen, J., Zhong, M., Li, J., Wang, D., Qian, T., Tu, H.: Effective deep attributed network representation learning with topology adapted smoothing. In: IEEE Transactions on Cybernetics (2021)

  11. Chen, M., Xu, Z., Weinberger, K. Q., Sha, F.: Marginalized denoising autoencoders for domain adaptation. In: ICML, pp. 1627–1634 (2012)

  12. Chen, X., Yu, Y., Jiang, F., Zhang, L., Gao, R., Gao, H.: Graph neural networks boosted personalized tag recommendation algorithm. In: IJCNN, pp. 1–8 (2020)

  13. Dai, T., Zhu, L., Wang, Y., Carley, K. M.: Attentive stacked denoising autoencoder with bi-lstm for personalized context-aware citation recommendation. IEEE/ACM Trans. Audio Speech Language Process 28, 553–568 (2020)

    Article  Google Scholar 

  14. De Lathauwer, L., De Moor, B., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253–1278 (2000)

    Article  MATH  Google Scholar 

  15. Du, J., Michalska, S., Subramani, S., Wang, H., Zhang, Y.: Neural attention with character embeddings for hay fever detection from twitter. Health Inf. Sci. Syst. 7(1), 1–7 (2019)

    Article  Google Scholar 

  16. Elhamifar, E., Sapiro, G., Yang, A., Sasrty, S. S.: A convex optimization framework for active learning. In: 2013 IEEE International Conference on Computer Vision, pp. 209–216 (2013)

  17. Fang, X., Pan, R., Cao, G., He, X., Dai, W.: Personalized tag recommendation through nonlinear tensor factorization using gaussian kernel. In: AAAI, pp. 439–445 (2015)

  18. Guan, Z., Bu, J., Mei, Q., Chen, C., Wang, C.: Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In: SIGIR, pp. 540–547 (2009)

  19. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)

  20. Hong, C., Yu, J., Wan, J., Tao, D., Wang, M.: Multimodal deep autoencoder for human pose recovery. IEEE Trans. Image Process. 24(12), 5659–5670 (2015)

    Article  MATH  Google Scholar 

  21. Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: Search and ranking. In: European semantic web conference, pp. 411–426 (2006)

  22. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM, pp. 263–272 (2008)

  23. Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in folksonomies. In: European Conference on Principles of Data Mining and Knowledge Discovery, pp. 506–514 (2007)

  24. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: ICLR, pp. 1–15 (2014)

  25. Kingma, D. P., Welling, M.: Auto-encoding variational bayes. In: ICLR (2014)

  26. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  27. Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: RecSys, pp. 61–68 (2009)

  28. Li, S., Kawale, J., Fu, Y.: Deep collaborative filtering via marginalized denoising auto-encoder. In: CIKM, pp. 811–820 (2015)

  29. Li, Z., Wang, X., Li, J., Zhang, Q.: Deep attributed network representation learning of complex coupling and interaction. Knowl Based Syst. 212, 106618 (2021)

    Article  Google Scholar 

  30. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. Internet Comput IEEE 7(1), 76–80 (2003)

    Article  Google Scholar 

  31. Mike, H., Jan, N.V.R., Aske, P.: A survey of deep meta-learning. Artif. Intell. Rev. 54, 4483–4541 (2021)

    Article  Google Scholar 

  32. Nguyen, H. T., Wistuba, M., Grabocka, J., Drumond, L. R., Schmidt-Thieme, L.: Personalized deep learning for tag recommendation. In: PAKDD, pp. 186–197 (2017)

  33. Nguyen, H. T., Wistuba, M., Schmidt-Thieme, L.: Personalized tag recommendation for images using deep transfer learning Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 705–720 (2017)

  34. Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: ICDM, pp. 502–511 (2008)

  35. Quintanilla, E., Rawat, Y., Sakryukin, A., Shah, M., Kankanhalli, M.: Adversarial learning for personalized tag recommendation. IEEE Trans. Multimed. 23, 1083–1094 (2021)

    Article  Google Scholar 

  36. Rendle, S., Balby Marinho, L., Nanopoulos, A., Schmidt-Thieme, L.: Learning optimal ranking with tensor factorization for tag recommendation. In: SIGKDD, pp. 727–736 (2009)

  37. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461 (2009)

  38. Rendle, S., Krichene, W., Zhang, L., Anderson, J.: Neural collaborative filtering vs. matrix factorization revisited. In: RecSys, pp. 240–248 (2020)

  39. Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: WSDM, pp. 81–90 (2010)

  40. Rifai, S., Vincent, P., Muller, X., Glorot, X., Bengio, Y.: Contractive auto-encoders: Explicit invariance during feature extraction. In: ICML, pp. 833–840 (2011)

  41. Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)

  42. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)

  43. Sedhain, S., Menon, A. K., Sanner, S., Xie, L.: Autorec: Autoencoders meet collaborative filtering. In: WWW, pp. 111–112 (2015)

  44. Song, X., Li, J., Tang, Y., Zhao, T., Chen, Y., Guan, Z.: Jkt: A joint graph convolutional network based deep knowledge tracing. Info. Sci. 580, 510–523 (2021)

    Article  Google Scholar 

  45. Sun, B., Zhu, Y., Xiao, Y., Xiao, R., Wei, Y.: Automatic question tagging with deep neural networks. IEEE Trans. Learn. Technol. 12(1), 29–43 (2018)

    Article  Google Scholar 

  46. Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: RecSys, pp. 43–50 (2008)

  47. Tang, S., Yao, Y., Zhang, S., Xu, F., Gu, T., Tong, H., Yan, X., Lu, J.: An integral tag recommendation model for textual content. 5109–5116 (2019)

  48. Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: ICML, pp. 1096–1103 (2008)

  49. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P. A.: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. JMLR 11(11), 3371–3408 (2010)

    MATH  Google Scholar 

  50. Wang, H., Shi, X., Yeung, D.Y.: Relational stacked denoising autoencoder for tag recommendation. In: AAAI, pp. 3052–3058 (2015)

  51. Wang, H., Wang, N., Yeung, D. Y.: Collaborative deep learning for recommender systems. In: KDD, pp. 1235–1244 (2015)

  52. Wang, R., Tao, D.: Non-local auto-encoder with collaborative stabilization for image restoration. IEEE Trans. Image Process. 25, 2117–2129 (2016)

    Article  MATH  Google Scholar 

  53. Wang, Z., Du, B., Guo, Y.: Domain adaptation with neural embedding matching. IEEE Trans. Neural Netw. Learn. Syst. 31(7), 2387–2397 (2020)

    Article  Google Scholar 

  54. Wang, Z., Du, B., Tu, W., Zhang, L., Tao, D.: Incorporating distribution matching into uncertainty for multiple kernel active learning. IEEE Trans. Knowl. Data Eng. 33(1), 128–142 (2021)

    Article  Google Scholar 

  55. Wei, L. C., Deng, Z. H.: A variational autoencoding approach for inducing cross-lingual word embeddings. In: IJCAI, pp. 4165–4171 (2017)

  56. Wu, Y., DuBois, C., Zheng, A. X., Ester, M.: Collaborative denoising auto-encoders for top-n recommender systems. In: WSDM, pp. 153–162 (2016)

  57. Wu, Y., Yao, Y., Xu, F., Tong, H., Lu, J.: Tag2word: Using tags to generate words for content based tag recommendation. In: CIKM, pp. 2287–2292 (2016)

  58. Xue, G., Zhong, M., Li, J., Chen, J., Zhai, C., Kong, R.: Dynamic network embedding survey. arXiv:2103.15447 (2021)

  59. Yin, J., Tang, M., Cao, J., Wang, H., You, M., Lin, Y.: Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web pp. 1–23 (2021)

  60. Yuan, J., Jin, Y., Liu, W., Wang, X.: Attention-based neural tag recommendation. In: DASFAA, pp. 350–365 (2019)

  61. Zheng, Q., Liu, G., Liu, A., Li, Z., Zheng, K., Zhao, L., Zhou, X.: Implicit relation-aware social recommendation with variational auto-encoder. World Wide Web (2021)

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Acknowledgements

The authors would like to acknowledge the support for this work from the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (17KJB520028), Tongda College of Nanjing University of Posts and Telecommunications (XK203XZ21001) and Future Network Scientific Research Fund Project(FNSRFP-2021-YB-54).

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Authors and Affiliations

  1. State Key Lab for Novel Software Technology, Nanjing University, Nanjing, People’s Republic of China

    Weibin Zhao, Lin Shang & Jiajun Chen

  2. TongDa College, Nanjing University of Posts and Telecommunications, Yangzhou, People’s Republic of China

    Yonghong Yu

  3. Department of Computer Science, Royal Holloway, University of London, Surrey, UK

    Li Zhang

  4. School of Information and Communication Technology, Griffith University, Queensland, Australia

    Can Wang

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  1. Weibin Zhao
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This article belongs to the Topical Collection: Special Issue on Decision Making in Heterogeneous Network Data Scenarios and Applications

Guest Editors: Jianxin Li, Chengfei Liu, Ziyu Guan, and Yinghui Wu

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Zhao, W., Shang, L., Yu, Y. et al. Personalized tag recommendation via denoising auto-encoder. World Wide Web 26, 95–114 (2023). https://doi.org/10.1007/s11280-021-00967-3

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