适用于RPA工具的卷积序列推荐算法

摘要/Abstract

摘要： 在机器人流程自动化(Robotic Process Automation， RPA)软件中，经常采用序列推荐系统让机器人完成判断、选择等人工处理的任务。然而常用的序列推荐系统受限于序列信息的提取困难等问题，难以得到广泛应用。为了解决这一问题，构建一种基于Inception的卷积序列推荐模型，把时间和潜在空间中的用户行为序列信息嵌入进一幅“图像”中，并通过动态和静态2种不同的卷积层提取其中的局部特征，全面地提取用户的短期兴趣偏好，同时将用户嵌入矩阵作为用户的长期兴趣偏好嵌入到卷积层的输出中，共同构建完整的用户兴趣偏好，提升推荐性能。通过在3种公开数据集MovieLens 1M、Gowalla、Steam上分别进行实验，验证了基于Inception的卷积序列推荐模型的性能优于最新的序列推荐模型，在Top-N序列推荐的3种评价指标中（精确率、召回率、平均AP值），平均提升幅度在10%左右，单个指标上的最大提升幅度为14%。

Abstract: In RPA (Robotic Process Automation) softwares, sequence recommendation systems are often used to complete manual processing tasks such as judgment and selection. However, the commonly used sequence recommendation system is limited by the difficulty of extracting sequence information, so it is difficult to be widely used. In order to solve this problem, this paper constructs a convolutional sequence recommendation model based on Inception. It embeds user behavior sequence information in time and latent space into an “image”, and extracts local features through dynamic and static convolutional layers. It can fully extract the user’s short-term interest preferences, and embed the user embedding matrix as the user’s long-term interest preferences into the output of the convolutional layer. They work together to build a complete set of user interest preferences and improve recommendation performance. Through experiments on three public data sets MovieLens 1M, Gowalla, and Steam, it is verified that the performance of the convolutional sequence recommendation model based on Inception is better than the latest sequence recommendation model. Among the three evaluation indicators of Top-N series (Precision@N, Recall@N, MAP), the average increase is about 10%, and the maximum increase on a single index is 14%.

Key words: sequential recommendation, convolutional neural network, Inception network, robotic process automation(RPA), user preference

候聪颖, 王鹏, 朱丽霞, 管晓宁. 适用于RPA工具的卷积序列推荐算法[J]. 计算机与现代化, 2021, 0(12): 96-102.

HOU Cong-ying, WANG Peng, ZHU Li-xia, GUAN Xiao-ning. Convolutional Sequence Recommendation Algorithm for RPA Softwares[J]. Computer and Modernization, 2021, 0(12): 96-102.

参考文献［26］

［1］	BEN SCHAFER J, FRANKOWSKI D, HERLOCKER J, et al. Collaborative filtering recommender systems［M］// The Adaptive Web: Methods and Strategies of Web Personalization. Springer, 2007:291-324.
［2］	PAZZANI M J, BILLSUS D. Content-based recommendation systems［M］// The Adaptive Web: Methods and Strategies of Web Personalization. Springer, 2007:325-341.
［3］	LIU Q, WU S, WANG D Y, et al. Context-aware sequential recommendation［C］// 2016 IEEE 16th International Conference on Data Mining (ICDM). 2016:1053-1058.
［4］	ZHANG S, TAY Y, YAO L N, et al. Next item recommendation with self-attentive metric learning［C］// The 33rd AAAI Conference on Artificial Intelligence. 2019:11-20.
［5］	HE R N, MCAULEY J. Fusing similarity models with Markov chains for sparse sequential recommendation［C］// 2016 IEEE 16th International Conference on Data Mining (ICDM). 2016:191-200.
［6］	CHEN X, XU H T, ZHANG Y F, et al. Sequential recommendation with user memory networks［C］// Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 2018:108-116.
［7］	GERS F A, SCHMIDHUBER J, CUMMINS F. Learning to forget: Continual prediction with LSTM［J］. Neural Computation, 2000,12(10):2451-2471.
［8］	KANG W C, MCAULEY J. Self-attentive sequential recommendation［C］// 2018 IEEE International Conference on Data Mining (ICDM). 2018:197-206.
［9］	SOH H, SANNER S, WHITE M, et al. Deep sequential recommendation for personalized adaptive user interfaces［C］// Proceedings of the 22nd International Conference on Intelligent User Interfaces. 2017:589-593.
［10］	PASRICHA R, MCAULEY J. Translation-based factorization machines for sequential recommendation［C］// Proceedings of the 12th ACM Conference on Recommender Systems. 2018:63-71.
［11］	WU C Y, AHMED A, BEUTEL A, et al. Recurrent recommender networks［C］// Proceedings of the 10th ACM International Conference on Web Search and Data Mining. 2017:495-503.
［12］	MILLER A, FISCH A, DODGE J, et al. Key-value memory networks for directly reading documents［C］// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016:1400-1409.
［13］	HUANG J, ZHAO W X, DOU H, et al. Improving sequential recommendation with knowledge-enhanced memory networks［C］// The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018:505-514.
［14］	YAN A, CHENG S, KANG W C, et al. CosRec: 2D convolutional neural networks for sequential recommendation［C］// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019:2173-2176.
［15］	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. 2018:565-573.
［16］	徐春晖,徐向东. 前馈型神经网络新学习算法的研究［J］. 清华大学学报(自然科学版), 1999,39(3):1-3.
［17］	SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions［C］// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. 2015:1-9.
［18］	WANG S J, CAO L B, WANG Y. A survey on session-based recommender systems［J］. arXiv preprint arXiv:1902.04864, 2019.
［19］	FANG H, GUO G B, ZHANG D N, et al. Deep learning-based sequential recommender systems: Concepts, algorithms, and evaluations［C］// International Conference on Web Engineering. 2019:574-577.
［20］	WU Y, DUBOIS C, ZHENG A X, et al. Collaborative denoising auto-encoders for Top-N recommender systems［C］// Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 2016:153-162.
［21］	KINGMA D P, BA J. Adam: A method for stochastic optimization［J］. arXiv preprint arXiv:1412.6980, 2014.
［22］	SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: A simple way to prevent neural networks from overfitting［J］. The Journal of Machine Learning Research, 2014,15(1):1929-1958.
［23］	CHO E, MYERS S A, LESKOVEC J. Friendship and mobility: User movement in location-based social networks［C］// Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011:1082-1090.
［24］	RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback［C］// Proceedings of the 25th Coference on Uncertainty in Artificial Intelligence. 2009:452-461.
［25］	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. 2010:811-820.
［26］	HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks［J］. arXiv preprint arXiv:1511.06939, 2015.

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