Similar articles

Abstract: session-based recommendation aims to predict the next item based on a user's limited interactions within a short period. Existing approaches use mainly recurrent neural networks (RNNs) or graph neural networks (GNNs) to model the sequential patterns or the transition relationships between items. However, such models either ignore the over-smoothing issue of GNNs, or directly use cross-entropy loss with a softmax layer for model optimization, which easily results in the over-fitting problem. To tackle the above issues, we propose a self-supervised graph learning with target-adaptive masking (SGL-TM) method. Specifically, we first construct a global graph based on all involved sessions and subsequently capture the self-supervised signals from the global connections between items, which helps supervise the model in generating accurate representations of items in the ongoing session. After that, we calculate the main supervised loss by comparing the ground truth with the predicted scores of items adjusted by our designed target-adaptive masking module. Finally, we combine the main supervised component with the auxiliary self-supervision module to obtain the final loss for optimizing the model parameters. Extensive experimental results from two benchmark datasets, Gowalla and Diginetica, indicate that SGL-TM can outperform state-of-the-art baselines in terms of Recall@20 and MRR@20, especially in short sessions.

融合自监督图学习与目标自适应屏蔽的会话型推荐方法

[1]Afouras T, Owens A, Chung JS, et al., 2020. Self-supervised learning of audio-visual objects from video. Proc 16^th European Conf, p.208-224.

[2]Chen M, Wei ZW, Huang ZF, et al., 2020. Simple and deep graph convolutional networks. Proc 37^th Int Conf on Machine Learning, p.1725-1735.

[3]Chen T, Kornblith S, Norouzi M, et al., 2020. A simple framework for contrastive learning of visual representations. Proc 37^th Int Conf on Machine Learning, p.1597-1607.

[4]Chen TW, Wong RCW, 2020. Handling information loss of graph neural networks for session-based recommendation. Proc 26^th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining, p.1172-1180.

[5]Cheng ZY, Shen JL, Zhu L, et al., 2017. Exploiting music play sequence for music recommendation. Proc 26^th Int Joint Conf on Artificial Intelligence, p.3654-3660.

[6]Choi M, Kim J, Lee J, et al., 2021. Session-aware linear item–item models for session-based recommendation. Proc Web Conf, p.2186-2197.

[7]Davidson J, Liebald B, Liu JN, et al., 2010. The YouTube video recommendation system. Proc 4^th ACM Conf on Recommender Systems, p.293-296.

[8]Hidasi B, Karatzoglou A, Baltrunas L, et al., 2016. Session-based recommendations with recurrent neural networks. Proc 4^th Int Conf on Learning Representations.

[9]Hjelm RD, Fedorov A, Lavoie-Marchildon S, et al., 2019. Learning deep representations by mutual information estimation and maximization. Proc 7^th Int Conf on Learning Representations.

[10]Kingma DP, Ba LJ, 2015. Adam: a method for stochastic optimization. Proc 3^rd Int Conf on Learning Representations.

[11]Kipf TN, Welling M, 2017. Semi-supervised classification with graph convolutional networks. Proc 5^th Int Conf on Learning Representations.

[12]Kong LP, de Masson d’Autume C, Yu L, et al., 2020. A mutual information maximization perspective of language representation learning. Proc 8^th Int Conf on Learning Representations.

[13]Li J, Ren PJ, Chen ZM, et al., 2017. Neural attentive session-based recommendation. Proc ACM Conf on Information and Knowledge Management, p.1419-1428.

[14]Li YJ, Tarlow D, Brockschmidt M, et al., 2016. Gated graph sequence neural networks. Proc 4^th Int Conf on Learning Representations.

[15]Liu Q, Zeng YF, Mokhosi R, et al., 2018. STAMP: short-term attention/memory priority model for session-based recommendation. Proc 24^th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining, p.1831-1839.

[16]Nie LQ, Li YQ, Feng FL, et al., 2020. Large-scale question tagging via joint question-topic embedding learning. ACM Trans Inform Syst, 38(2):20.

[17]Nie LQ, Jiao FK, Wang WJ, et al., 2021. Conversational image search. IEEE Trans Image Process, 30:7732-7743.

[18]Pan ZQ, Cai F, Chen WY, et al., 2020. Star graph neural networks for session-based recommendation. Proc 29^th ACM Int Conf on Information & Knowledge Management, p.1195-1204.

[19]Pan ZQ, Cai F, Chen WY, et al., 2022. Graph co-attentive session-based recommendation. ACM Trans Inform Syst, 40(4):67.

[20]Qiu RH, Li JJ, Huang Z, et al., 2019. Rethinking the item order in session-based recommendation with graph neural networks. Proc 28^th ACM Int Conf on Information and Knowledge Management, p.579-588.

[21]Qiu RH, Huang Z, Li JJ, et al., 2020. Exploiting cross-session information for session-based recommendation with graph neural networks. ACM Trans Inform Syst, 38(3):22.

[22]Rendle S, Freudenthaler C, Schmidt-Thieme L, 2010. Factorizing personalized Markov chains for next-basket recommendation. Proc 19^th Int Conf on World Wide Web, p.811-820.

[23]Rumelhart DE, Hinton GE, Williams RJ, 1986. Learning representations by back-propagating errors. Nature, 323(6088):533-536.

[24]Shani G, Heckerman D, Brafman RI, 2005. An MDP-based recommender system. J Mach Learn Res, 6:1265-1295.

[25]Singhal A, Sinha P, Pant R, 2017. Use of deep learning in modern recommendation system: a summary of recent works. Int J Comput Appl, 180(7):17-22.

[26]Tan YK, Xu XX, Liu Y, 2016. Improved recurrent neural networks for session-based recommendations. Proc 1^st Workshop on Deep Learning for Recommender Systems, p.17-22.

[27]Tang JX, Wang K, 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. Proc 11^th ACM Int Conf on Web Search and Data Mining, p.565-573.

[28]Veličković P, Cucurull G, Casanova A, et al., 2018. Graph attention networks. Proc 6^th Int Conf on Learning Representations.

[29]Wang SJ, Cao LB, Wang Y, et al., 2022. A survey on session-based recommender systems. ACM Comput Surv, 54(7):154.

[30]Wang X, He XN, Wang M, et al., 2019. Neural graph collaborative filtering. Proc 42^nd Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.165-174.

[31]Wang XN, Tan QM, 2020. DAN: a deep association neural network approach for personalization recommendation. Front Inform Technol Electron Eng, 21(7):963-980.

[32]Wang ZY, Wei W, Cong G, et al., 2020. Global context enhanced graph neural networks for session-based recommendation. Proc 43^rd Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.169-178.

[33]Wu S, Tang YY, Zhu YQ, et al., 2019. Session-based recommendation with graph neural networks. Proc 33^rd AAAI Conf on Artificial Intelligence, p.346-353.

[34]Xia X, Yin HZ, Yu JL, et al., 2021a. Self-supervised graph co-training for session-based recommendation. Proc 30^th ACM Int Conf on Information & Knowledge Management, p.2180-2190.

[35]Xia X, Yin HZ, Yu JL, et al., 2021b. Self-supervised hyper- graph convolutional networks for session-based recommendation. Proc 35^th AAAI Conf on Artificial Intelligence, p.4503-4511.

[36]Xie X, Sun F, Liu ZY, et al., 2022. Contrastive learning for sequential recommendation. Proc IEEE 38^th Int Conf on Data Engineering, p.1259-1273.

[37]Xin X, Karatzoglou A, Arapakis I, et al., 2020. Self-supervised reinforcement learning for recommender systems. Proc 43^rd Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.931-940.

[38]Xu CF, Zhao PP, Liu YC, et al., 2019. Graph contextualized self-attention network for session-based recommendation. Proc 28^th Int Joint Conf on Artificial Intelligence, p.3940-3946.

[39]Yao TS, Yi XY, Cheng DZ, et al., 2021. Self-supervised learning for large-scale item recommendations. Proc 30^th ACM Int Conf on Information & Knowledge Management, p.4321-4330.

[40]Yuan FJ, Karatzoglou A, Arapakis I, et al., 2019. A simple convolutional generative network for next item recommendation. Proc 12^th ACM Int Conf on Web Search and Data Mining, p.582-590.

[41]Zhang JQ, Zhao Y, Saleh M, et al., 2020. PEGASUS: pre-training with extracted gap-sentences for abstractive summarization. Proc 37^th Int Conf on Machine Learning, p.11328-11339.

[42]Zheng JW, Ma QL, Gu H, et al., 2021. Multi-view denoising graph auto-encoders on heterogeneous information networks for cold-start recommendation. Proc 27^th ACM SIGKDD Conf on Knowledge Discovery & Data Mining, p.2338-2348.

[43]Zhou K, Wang H, Zhao WX, et al., 2020. S³-Rec: self-supervised learning for sequential recommendation with mutual information maximization. Proc 29^th ACM Int Conf on Information & Knowledge Management, p.1893-1902.