Abstract
The recommender systems have long been studied in the literature. The collaborative filtering is one of the most widely adopted recommendation techniques which is usually applied to the explicit data, e.g., rating scores. However, the implicit data, e.g., click data, is believed to be able to discover user’s latent preferences. Consequently, a number of research attempts have been made toward this issue. To the best of our knowledge, this paper is the first attempt to adapt the Wasserstein autoencoders to collaborative filtering problem. Particularly, we propose a new loss function by introducing an \(L_1\) regularization term to learn a sparse low-rank representation form to represent latent variables. Then, we carefully design (1) a new cost function to minimize the data reconstruction error, and (2) the appropriate distance metrics for the calculation of KL divergence between the learned distribution of latent variables and the underlying true data distribution. Rigorous experiments are performed on three widely adopted datasets. Both the state-of-the-art approaches, e.g., Mult-VAE and Mult-DAE, and the baseline models are evaluated on these datasets. The promising experimental results demonstrate that the proposed approach is superior to the compared approaches with respect to evaluation criteria Recall@R and NDCG@R.
Similar content being viewed by others
References
Arévalo J, Duque JR, Creatura M (2018) A missing information loss function for implicit feedback datasets. arXiv:1805.00121
Boyd S (2011) Alternating direction method of multipliers. In: Talk at NIPS workshop on optimization and machine learning
Chen Y, de Rijke M (2018) A collective variational autoencoder for top-\(n\) recommendation with side information. arXiv:1807.05730
Davidson J, Liebald B, Liu J, Nandy P, Van Vleet T, Gargi U, Gupta S, He Y, Lambert M, Livingston B, et al (2010) The youtube video recommendation system. In: Proceedings of the fourth ACM conference on Recommender systems. ACM, pp 293–296
Dong X, Yu L, Wu Z, Sun Y, Yuan L, Zhang F (2017) A hybrid collaborative filtering model with deep structure for recommender systems. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, February 4–9, 2017, San Francisco, California, USA., pp 1309–1315
Georgiev K, Nakov P (2013) A non-IID framework for collaborative filtering with restricted Boltzmann machines. In: International conference on international conference on machine learning, pp 1148–1156
Graves A (2011) Practical variational inference for neural networks. In: Advances in neural information processing systems, pp 2348–2356
Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: SIGKDD. ACM, pp 855–864
Guo H, Tang R, Ye Y, Li Z, He X (2017) DeepFM: a factorization-machine based neural network for CTR prediction. In: IJCAI, pp 1725–1731
Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: NeurIPS, pp 1024–1034
Herlocker JL, Konstan JA, Borchers A, Riedl J (2017) An algorithmic framework for performing collaborative filtering. In: ACM SIGIR forum, vol 51. ACM, pp 227–234
Herrada OC (2008) Music recommendation and discovery in the long tail. In: Ceedings of international congress on electron microscopy methods enzymol, vol 11, no 1, pp 7–8
Joachims T, Granka L, Pan B, Hembrooke H, Gay G (2017) Accurately interpreting click through data as implicit feedback. In: ACM SIGIR forum, vol 51. ACM, pp 4–11
Kingma DP, Welling M (2013) Auto-encoding variational Bayes. arXiv:1312.6114
Koren Y, Bell R (2015) Advances in collaborative filtering. In: Recommender systems handbook. Springer, pp 77–118
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 8:30–37
Kuchaiev O, Ginsburg B (2017) Training deep autoencoders for collaborative filtering. arXiv:1708.01715
Levy M, Jack K (2013) Efficient top-n recommendation by linear regression. In: RecSys large scale recommender systems workshop
Lian J, Zhou X, Zhang F, Chen Z, Xie X, Sun G (2018) xDeepFM: combining explicit and implicit feature interactions for recommender systems. In: SIGKDD. ACM, pp 1754–1763
Liang D, Krishnan RG, Hoffman MD, Jebara T (2018) Variational autoencoders for collaborative filtering. arXiv:1802.05814
Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B (2016) Adversarial autoencoders. arXiv:1511.05644
Mnih A, Salakhutdinov RR (2008) Probabilistic matrix factorization. In: Advances in neural information processing systems, pp 1257–1264
Ning X, Karypis G (2011) Slim: sparse linear methods for top-n recommender systems. In: Proceedings of ICDM, pp 497–506
Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: online learning of social representations. In: SIGKDD. ACM, pp 701–710
Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In: Recommender systems handbook. Springer, pp 1–34
Shi Y, Larson M, Hanjalic A (2014) Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput Surv 47(1):1–45
Sun Z, Zhang X, Ye Y, Chu X, Liu Z (2020) A probabilistic approach towards an unbiased semi-supervised cluster tree. Knowl-Based Syst 192:300–306
Tolstikhin I, Bousquet O, Gelly S, Schoelkopf B (2017) Wasserstein auto-encoders. arXiv:1711.01558
Wang D, Qian X, Quek C, Tan A-H, Miao C, Zhang X, Ng GS, Zhou Y (2018) An interpretable neural fuzzy inference system for predictions of underpricing in initial public offerings. Neurocomputing 319:102–117
Wu L, Sun P, Hong R, Fu Y, Wang X, Wang M (2019) SocialGCN: an efficient graph convolutional network based model for social recommendation. In: SIGIR
Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the ninth ACM international conference on web search and data mining. ACM, pp 153–162
Xu Z, Chen C, Lukasiewicz T, Miao Y, Meng X (2016) Tag-aware personalized recommendation using a deep-semantic similarity model with negative sampling. In: ACM international on conference on information and knowledge management, pp 1921–1924
Yi M (2017) Collaborative filtering. Comput Sci 57(4):189–189
Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems. In: SIGKDD. ACM, pp 974–983
Zhang X, Liu H, Chen X, Zhong J, Wang D (2020) A novel hybrid deep recommendation system to differentiate users preference and items attractiveness. Inf Sci 519:306–316
Zhao S, Song J, Ermon S (2017) InfoVAE: Information maximizing variational autoencoders. arXiv:1706.02262
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
No conflict of interest exits in the submission of this manuscript.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhang, X., Zhong, J. & Liu, K. Wasserstein autoencoders for collaborative filtering. Neural Comput & Applic 33, 2793–2802 (2021). https://doi.org/10.1007/s00521-020-05117-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-020-05117-w