Abstract
Collaborative filtering (CF) is the most fundamental technique in recommender systems, which reveals user preference by implicit feedback. Generally, binary cross-entropy or bayesian personalized ranking are usually employed as the loss function to optimize model parameters. Recently, the sampled softmax loss has been proposed to enhance the sampling efficiency, which adopts an in-batch sample strategy. However, it suffers from the sample bias issue, which unavoidably introduces false negative instances, resulting inaccurate representations of users’ genuine interests. To address this problem, we propose a debiased contrastive loss, incorporating a bias correction probability to alleviate the sample bias. We integrate the proposed method into several matrix factorizations (MF) and graph neural network-based (GNN) recommendation models. Besides, we theoretically analyze the effectiveness of our methods in automatically mining the hard negative instances. Experimental results on three public benchmarks demonstrate that the proposed debiased contrastive loss can augment several existing MF and GNN-based CF models and outperform popular learning objectives in the recommendation. Additionally, we demonstrate that our method substantially enhances training efficiency.
This work was partially supported by the National Natural Science Foundation of China (No. 61977002), the German Federal Ministry of Education and Research (BMBF) under Grant No. 01IS18036A and the State Key Laboratory of Software Development Environment of China (No. SKLSDE-2022ZX-14).
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References
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: The 42nd International ACM SIGIR Conference (2019)
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: KDD, pp. 974–983. ACM (2018)
Chen, L., Wu, L., Hong, R., Zhang, K., Wang, M.: Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: AAAI, pp. 27–34. AAAI Press (2020)
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: simplifying and powering graph convolution network for recommendation. In: SIGIR, pp. 639–648. ACM (2020)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. CoRR, abs/1205.2618 (2012)
Barbano, C.A., Dufumier, B., Tartaglione, E., Grangetto, M., Gori, P.: Unbiased supervised contrastive learning. In: The Eleventh International Conference on Learning Representations (2023)
Zhou, C., Ma, J., Zhang, J., Zhou, J., Yang, H.: Contrastive learning for debiased candidate generation in large-scale recommender systems (2021)
Wu, J., et al.: On the effectiveness of sampled softmax loss for item recommendation. arXiv preprint arXiv:2201.02327 (2022)
Chuang, C.-Y., Robinson, J., Lin, Y.-C., Torralba, A., Jegelka, S.: Debiased contrastive learning. CoRR, abs/2007.00224 (2020)
Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, Melbourne, Australia, vol. 17, pp. 3203–3209 (2017)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. CoRR, abs/1708.05031 (2017)
van den Berg, R., Thomas, N.K., Welling, M.: Graph convolutional matrix completion. CoRR, abs/1706.02263 (2017)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (Poster). OpenReview.net (2017)
Shan, Y., Hoens, T.R., Jiao, J., Wang, H., Yu, D., Mao, J.C.: Deep crossing: web-scale modeling without manually crafted combinatorial features. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 255–262 (2016)
Chuang, C.-Y., Robinson, J., Lin, Y.-C., Torralba, A., Jegelka, S.: Debiased contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 8765–8775 (2020)
Wang, F., Liu, H.: Understanding the behaviour of contrastive loss. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2495–2504 (2021)
Sun, F., et al.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441–1450 (2019)
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Liu, Z., Ma, Y., Li, H., Hildebrandt, M., Ouyang, Y., Xiong, Z. (2023). Debiased Contrastive Loss for Collaborative Filtering. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14119. Springer, Cham. https://doi.org/10.1007/978-3-031-40289-0_8
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