小波卷积增强的对比学习推荐算法

doi:10.19678/j.issn.1000-3428.0064747

摘要/Abstract

摘要： 推荐算法是一种用于解决信息过载问题的方法，序列化推荐通过建模用户购买的物品序列预测下一个物品。现有的序列化推荐算法通常忽视用户行为序列中的噪声、跨序列信息和物品间的组合依赖等问题，导致推荐性能受限。为此，提出一种小波卷积增强的对比学习推荐算法WCLR。利用数据的内在相关性获得自监督信号，并根据预训练的方法来增强数据表示。给出3个辅助的自监督学习任务，利用信息最大化原理学习属性、物品、序列与邻居序列的相关性，通过互信息最大化提供一种统一的方式描述不同类型数据间的相关性。由于小波卷积网络能提取物品的组合依赖，降低用户交互序列中的噪声，设计一个多核小波卷积模块，通过多尺寸用户序列多方面捕获用户的潜在兴趣，将自监督学习和小波卷积融入到推荐算法模型中，降低序列数据稀疏性和噪声，提高推荐精度。在LastFM、Beauty和Toys 3个数据集上的实验结果表明，与8个序列化推荐模型相比，WCLR算法的命中率、归一化折损累计增益和平均倒数秩分别提升了3.30%、1.47%和2.17%。

Abstract: Recommendation algorithms are methods of solving information overload，which use sequential recommendations to predict the next item by modeling the sequence of items purchased by the user.Existing sequential recommendation algorithms ignore the noise and union-level influence between items，which results in limited recommendation performance.Therefore，this study proposes a Wavelet convolution enhanced Contrastive Learning Recommendation（WCLR） algorithm.The model exploits the intrinsic data correlation to obtain self-supervised signals and enhances the data representation using pre-trained methods.Three auxiliary self-supervised learning tasks are designed，which use the principle of information maximization to learn the correlation between the attribute，item， sequence，and neighbor sequence.Mutual Information Maximization（MIM） provides a unified approach to describing the correlation between the different types of data.Wavelet convolution extracts the combinatorial dependencies of the items and reduces the noise in the user sequence.The model captures the potential interest of the users through the multiple aspects of the multi-size user sequences by designing a multi-core wavelet convolution module.The WCLR algorithm integrates self-supervised learning and wavelet convolution into the model to reduce the sparsity and noise of the sequence data and improve recommendation accuracy.The experimental results from the LastFM，Beauty，and Toys data sets show that the Hit Rate（HR），Normalization Discounted Cumulative Gain（NDCG），and Mean Reciprocal Rank（MRR） of the WCLR algorithm model increased by 3.30%，1.47%，and 2.17%，respectively，compared to those of the eight serialized recommended models.

Key words: recommendation algorithm, sequential recommendation, wavelet transform, wavelet convolution network, self-supervised learning

中图分类号:

TP18

许凤, 杨兴耀, 于炯, 李梓杨, 李晨瑜, 张君. 小波卷积增强的对比学习推荐算法[J]. 计算机工程, 2023, 49(5): 105-111,121.

XU Feng, YANG Xingyao, YU Jiong, LI Ziyang, LI Chenyu, ZHANG Jun. Wavelet Convolution Enhanced Contrastive Learning Recommendation Algorithm[J]. Computer Engineering, 2023, 49(5): 105-111,121.

http://www.ecice06.com/CN/Y2023/V49/I5/105

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参考文献

[1] MENG W J,YANG D Q,XIAO Y H.Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,USA:ACM Press,2020:1091-1100.
[2] TANG J X,WANG K.Personalized top-N sequential recommendation via convolutional sequence embedding[C]//Proceedings of the 11th ACM International Conference on Web Search and Data Mining.New York,USA:ACM Press,2018:565-573.
[3] KANG W C,MCAULEY J.Self-attentive sequential recommendation[C]//Proceedings of IEEE International Conference on Data Mining.Washington D.C.,USA:IEEE Press,2018:197-206.
[4] RENDLE S,FREUDENTHALER C,SCHMIDT-THIEME L.Factorizing personalized Markov chains for next-basket recommendation[C]//Proceedings of the 19th International Conference on World Wide Web.New York,USA:ACM Press,2010:811-820.
[5] SONG W P,SHI C C,XIAO Z P,et al.Autoint:automatic feature interaction learning via self-attentive neural networks[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.New York,USA:ACM Press,2019:1161-1170.
[6] RENDLE S.Factorization machines[C]//Proceedings of IEEE International Conference on Data Mining.Washington D.C.,USA:IEEE Press,2011:995-1000.
[7] HUANG J,ZHAO W X,DOU H J,et al.Improving sequential recommendation with knowledge-enhanced memory networks[C]//Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval.New York,USA:ACM Press,2018:505-514.
[8] QUADRANA M,KARATZOGLOU A,HIDASI B,et al.Personalizing session-based recommendations with hierarchical recurrent neural networks[C]//Proceedings of the 11th ACM Conference on Recommender Systems.New York,USA:ACM Press,2017:130-137.
[9] WANG P F,FAN Y,XIA L,et al.KERL:a knowledge-guided reinforcement learning model for sequential recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,USA:ACM Press,2020:209-218.
[10] XIA X,YIN H Z,YU J L,et al.Self-supervised hypergraph convolutional networks for session-based recommendation[J].Artificial Intelligence,2021,35(5):4503-4511.
[11] LIU P J,ZHANG H Z,LIAN W,et al.Multi-level wavelet convolutional neural networks[J].IEEE Access,2019,7:74973-74985.
[12] WANG J Y,WANG Z,LI J F,et al.Multilevel wavelet decomposition network for interpretable time series analysis[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York,USA:ACM Press,2018:2437-2446.
[13] 梁小慧,郭晟楠,万怀宇.基于自适应小波分解的时间序列分类方法[J].计算机工程,2022,48(4):81-88,98. LIANG X H,GUO S N,WAN H Y.Time series classification method based on adaptive wavelet decomposition[J].Computer Engineering,2022,48(4):81-88,98.(in Chinese)
[14] XU X X,FANG Z W,YU Q,et al.Gating-adapted wavelet multiresolution analysis for exposure sequence modeling in CTR prediction[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,USA:ACM Press,2022:1-5.
[15] YAO T S,YI X Y,CHENG D Z,et al.Self-supervised learning for large-scale item recommendations[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management.New York,USA:ACM Press,2021:4321-4330.
[16] MA J X,ZHOU C,YANG H X,et al.Disentangled self-supervision in sequential recommenders[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York,USA:ACM Press,2020:483-491.
[17] QIU R H,HUANG Z,YIN H Z,et al.Contrastive learning for representation degeneration problem in sequential recommendation[C]//Proceedings of the 15th ACM International Conference on Web Search and Data Mining.New York,USA:ACM Press,2022:813-823.
[18] HIDASI B,KARATZOGLOU A,BALTRUNAS L,et al.Session-based recommendations with recurrent neural networks[C]//Proceedings of International Conference on Learning Representations.Washington D.C.,USA:IEEE Press,2016:1-10.
[19] XIE R B,LIU Q,WANG L D,et al.Contrastive cross-domain recommendation in matching[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.New York,USA:ACM Press,2022:4226-4236.
[20] ZHANG Y X,LIU Y,XU Y H,et al.Enhancing sequential recommendation with graph contrastive learning[C]//Proceedings of the 31st International Joint Conference on Artificial Intelligence.Washington D.C.,USA:IEEE Press,2022:1-8.
[21] MA Y S,HE Y Z,ZHANG A,et al.CrossCBR:cross-view contrastive learning for bundle recommendation[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.New York,USA:ACM Press,2022:1233-1241.
[22] CHEN Y J,LIU Z W,LI J,et al.Intent contrastive learning for sequential recommendation[C]//Proceedings of ACM Web Conference.New York,USA:ACM Press,2022:2172-2182.
[23] SUN F,LIU J,WU J,et al.BERT4Rec:sequential recommendation with bidirectional encoder representations from transformer[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.New York,USA:ACM Press,2019:1441-1450.
[24] ZHANG T T,ZHAO P P,LIU Y C,et al.Feature-level deeper self-attention network for sequential recommendation[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence.Washington D.C.,USA:IEEE Press,2019:4320-4326.
[25] ZHOU K,YU H,ZHAO W X,et al.Filter-enhanced MLP is all you need for sequential recommendation[C]//Proceedings of ACM Web Conference.New York,USA:ACM Press,2022:2388-2399.
[26] ZHOU K,WANG H,ZHAO W X,et al.S3Rec:self-supervised learning for sequential recommendation with mutual information maximization[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management.New York,USA:ACM Press,2020:1893-1902.

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