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AutoFM: an efficient factorization machine model via probabilistic auto-encoders

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Abstract

Studies show that conventional factorization machines (FMs) have low performance in capturing both local and global structures of user–item correlation simultaneously. Recently, deep neural networks (DNNs) have been applied to improve FMs. However, DNNs increase the complexity of the training process. Moreover, DNN-based FMs ignore the integration of neighborhood-based approaches. An efficient method called factorization machine model via probabilistic auto-encoders (AutoFM) is proposed to resolve this issue in the present study. The proposed AutoFM can extract non-trivial and local structures characteristics from user–user/item–item co-occurrence pairs by integrating a low-complexity probabilistic auto-encoder. Furthermore, it supports both explicit and implicit feedback datasets. Extensive experiments on four real-world datasets demonstrate the effectiveness of the proposed method. The results show that the AutoFM outperforms the current state-of-the-art methods in rating prediction tasks. Compared with the DNN-based FM models, the proposed AutoFM model improves the item ranking at least 1.16%\(\sim\) 4.37%.

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References

  1. Ebesu T, Shen B, Fang Y (2018) Collaborative memory network for recommendation systems. In: The 41st International ACM SIGIR Conference on research and development in information retrieval, pp 515–524

  2. Rendle S (2010) Factorization machines, in: 2010 IEEE International conference on data mining. IEEE, pp 995–1000

  3. Rendle S (2012) Factorization machines with libfm. Acm Trans Intell Syst Technol 3(3):1–22

    Article  Google Scholar 

  4. Koren Y (2008) Factorization meets the neighborhood: A multifaceted collaborative filtering model. In: ACM SIGKDD International Conference on knowledge discovery and data mining, pp 426–434

  5. Koren Y (2010) Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Trans Knowl Discov Data 4(1):1–24

    Article  Google Scholar 

  6. He X, Chua T-S (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

  7. Liu C, Zhang T, Zhao P, Zhou J, Sun J (2017) Locally linear factorization machines. In: Proceedings of the 26th International joint conference on artificial intelligence, pp 2294–2300

  8. Guo H, Tang R, Ye Y, Li Z, He X (2017) DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint. arXiv:1703.04247

  9. Bengio Y (2009) Learning deep architectures for ai, Foundations & Trends®. Mach Learn 2(1):1–127

    Article  MathSciNet  Google Scholar 

  10. Zhuang F, Zhang Z, Qian M, Shi C, Xie X, He Q (2017) Representation learning via dual-autoencoder for recommendation. Neural Netw 90:83–89

    Article  Google Scholar 

  11. Huang T, Zhang D, Bi L (2020) Neural embedding collaborative filtering for recommender systems. Neural Comput Appl 32(22):17043–17057

    Article  Google Scholar 

  12. Zhang S, Yao L, Tay Y, Xu X, Zhang X, Zhu L (2018) Metric factorization: recommendation beyond matrix factorization. arXiv preprint. arXiv:1802.04606

  13. Mikolov T, Sutskever I, Kai C, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Proc Syst 26:3111–3119

    Google Scholar 

  14. Landgraf AJ, Bellay J, Word2vec skip-gram with negative sampling is a weighted logistic pca, arXiv preprint arXiv:1705.09755

  15. Ozsoy MG, From word embeddings to item recommendation, arXiv preprint arXiv:1601.01356

  16. Barkan O, Koenigstein N (2016) Item2vec: neural item embedding for collaborative filtering. In: Machine learning for signal processing (MLSP), 2016 IEEE 26th international workshop on, IEEE, pp 1–6

  17. Liang D, Altosaar J, Charlin L, Blei DM (2016) Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In: Proceedings of the 10th ACM conference on recommender systems, pp 59–66

  18. Liang H, Baldwin T (2015) A probabilistic rating auto-encoder for personalized recommender systems. In: Proceedings of the 24th ACM International on conference on information and knowledge management, pp 1863–1866

  19. Rendle S, Gantner Z, Freudenthaler C, Schmidt-Thieme L (2011) Fast context-aware recommendations with factorization machines. In: Proceedings of the 34th international ACM SIGIR conference on research and development in Information Retrieval, pp 635–644

  20. Baltrunas L, Church K, Karatzoglou A, Oliver N, Frappe: Understanding the usage and perception of mobile app recommendations in-the-wild, arXiv preprint arXiv:1505.03014

  21. Mingdang Tang ZZ, Zhang Tingting (2018) Qos-aware web service recommendation based on factorization machines. Chin J Comput 41(6):114–127

    Google Scholar 

  22. Da C, He X, Nie L, Wei X, Chua TS (2017) Cross-platform app recommendation by jointly modeling ratings and texts. Acm Trans Inf Syst 35(4):1–27

    Google Scholar 

  23. Oentaryo RJ, Lim EP, Low JW, Lo D, Finegold M (2014) Predicting response in mobile advertising with hierarchical importance-aware factorization machine. In: Acm International conference on web search and data mining

  24. Salakhutdinov R, Mnih A, Hinton G (2007) Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on machine learning, pp 791–798

  25. Lee J, Kim S, Lebanon G, Singer Y, Bengio S (2016) Llorma: Local low-rank matrix approximation. J Mach Learn Res 17(1):442–465

    MathSciNet  MATH  Google Scholar 

  26. Sedhain S, Menon AK, Sanner S, Xie L (2015) Autorec: Autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on World Wide Web, pp 111–112

  27. Berg Rvd, Kipf TN, Welling M, Graph convolutional matrix completion, arXiv preprint arXiv:1706.02263

  28. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  29. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285–295

  30. Kingma DP, Ba J, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980

  31. Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In: Recommender systems handbook. Springer, Boston, MA, pp 1–35

  32. Kabbur S, Ning X, Karypis G (2013) Fism: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 659–667

  33. He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering, in: Proceedings of the 26th international conference on world wide web, pp 173–182

  34. Haijun Zhang, Yanfang Sun, Mingbo Zhao, Tommy WSC and, Bridging user interest to item content for recommender systems: an optimization model., IEEE transactions on cybernetics

  35. Aslanian E, Radmanesh M, Jalili M (2016) Hybrid recommender systems based on content feature relationship. IEEE transactions on industrial informatics 1–1

  36. Castells P, Wang J, Lara R, Zhang D (2014) Introduction to the special issue on diversity and discovery in recommender systems. ACM Trans Intell Syst Technol 5(4):1–3

    Google Scholar 

  37. Clarke CL, Kolla M, Cormack GV, Vechtomova O, Ashkan A, Büttcher S, MacKinnon I (2008) Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval, pp 659–666

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Authors and Affiliations

  1. School of Information Science and Engineering, Xiamen University, Xiamen, 361005, People’s Republic of China

    Tianlin Huang & Defu Zhang

  2. School of Physics and Telecommunications Engineering, Yulin Normal University, Yulin, 537000, People’s Republic of China

    Lvqing Bi

  3. College of Information and Smart Electromechanical Engineering, Xiamen Huaxia University, Xiamen, 361024, People’s Republic of China

    Ning Wang

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  1. Tianlin Huang
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  2. Lvqing Bi
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  3. Ning Wang
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  4. Defu Zhang
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Correspondence to Lvqing Bi or Defu Zhang.

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Huang, T., Bi, L., Wang, N. et al. AutoFM: an efficient factorization machine model via probabilistic auto-encoders. Neural Comput & Applic 33, 9451–9466 (2021). https://doi.org/10.1007/s00521-021-05705-4

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