基于双重最相关注意力网络的协同过滤推荐算法

doi:10.11772/j.issn.1001-9081.2020061023

计算机应用 ›› 2020, Vol. 40 ›› Issue (12): 3445-3450.DOI: 10.11772/j.issn.1001-9081.2020061023

• 2020年中国粒计算与知识发现学术会议(CGCKD 2020) • 上一篇下一篇

基于双重最相关注意力网络的协同过滤推荐算法

张文龙^1,2, 钱付兰^1,2, 陈洁^1,2, 赵姝^1,2, 张燕平^1,2

1. 安徽大学计算机科学与技术学院, 合肥 230601;
2. 计算智能与信号处理教育部重点实验室(安徽大学), 合肥 230601

收稿日期:2020-06-12 修回日期:2020-09-16 发布日期:2020-10-20 出版日期:2020-12-10
通讯作者: 钱付兰(1978-),女,安徽蚌埠人,副教授,博士,主要研究方向:粒计算、社交网络、推荐系统。qianfulan@hotmail.com
作者简介:张文龙(1996-),男,安徽阜阳人,硕士研究生,主要研究方向:深度学习、推荐系统;陈洁(1982-),女,安徽巢湖人,副教授,博士,主要研究方向:智能计算、机器学习、三支决策;赵姝(1979-),女,安徽巢湖人,教授,博士,主要研究方向:粒计算、商空间理论、机器学习;张燕平(1962-),女,安徽合肥人,教授,博士,主要研究方向:智能计算、粒计算、商空间理论
基金资助:
国家自然科学基金资助项目（61702003）；安徽省自然科学基金资助项目（1808085MF175）。

Collaborative filtering recommendation algorithm based on dual most relevant attention network

ZHANG Wenlong^1,2, QIAN Fulan^1,2, CHEN Jie^1,2, ZHAO Shu^1,2, ZHANG Yanping^1,2

1. School of Computer Science and Technology, Anhui University, Hefei Anhui 230601, China;
2. Key Laboratory of Intelligent Computing&Signal Processing, Ministry of Education(Anhui University), Hefei Anhui 230601, China

Received:2020-06-12 Revised:2020-09-16 Online:2020-10-20 Published:2020-12-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China （61702003）， the Natural Science Foundation of Anhui Province （1808085MF175）.

摘要/Abstract

摘要： 基于项目的协同过滤从用户的历史交互项目中学习用户偏好，根据用户的偏好推荐相似的新项目。现有的协同过滤方法认为用户所交互的一组历史项目对用户的影响是相同的，并且将所有历史交互项目在对目标项目作预测时的贡献看作是相同的，导致这些推荐方法的准确性受限。针对上述问题，提出了一种基于双重最相关注意力网络的协同过滤推荐算法，该算法包含两层注意力网络。首先，使用项目级注意力网络为不同历史项目分配不同的权重来捕获用户历史交互项目中最相关的项目；然后，使用项目交互级注意力网络感知不同历史项目与目标项目之间的交互关联度；最后，通过两层注意力网络的使用来同时捕获用户在历史交互项目上和目标项目上的细粒度偏好，从而更好地进行下一步推荐工作。在MovieLens和Pinterest两个真实数据集上进行实验，实验结果表明，所提算法在推荐命中率上与基准模型基于深度学习的项目协同过滤（DeepICF）算法相比分别提升了2.3个百分点和1.5个百分点，验证了该算法在为用户进行个性化推荐上的有效性。

关键词: 推荐系统, 协同过滤, 深度学习, 隐式反馈, 注意力机制

Abstract: Item-based collaborative filtering learns user preferences from the user's historical interaction items and recommends similar new items based on the user's preferences. The existing collaborative filtering methods assume that a set of historical items that user has interacted with have the same impact on user, and all historical interaction items are considered to have the same contribution to the prediction of target item, which limits the accuracy of these recommendation methods. In order to solve the problems, a new collaborative filtering recommendation algorithm based on dual most relevant attention network was proposed, which contained two attention network layers. Firstly, the item-level attention network was used to assign different weights to different historical items in order to capture the most relevant items in the user historical interaction items. Then, the item-interaction-level attention network was used to perceive the correlation degrees of the interactions between the different historical items and the target item. Finally, the fine-grained preferences of users on the historical interaction items and the target item were simultaneously captured through the two attention network layers, so as to make the better recommendations for the next step. The experiments were conducted on two real datasets of MovieLens and Pinterest. Experimental results show that, the proposed algorithm improves the recommendation hit rate by 2.3 percentage points and 1.5 percentage points respectively compared with the benchmark model Deep Item-based Collaborative Filtering (DeepICF) algorithm, which verifies the effectiveness of the proposed algorithm on making personalized recommendations for users.

Key words: recommender system, collaborative filtering, deep learning, implicit feedback, attention mechanism

中图分类号:

TP183

张文龙, 钱付兰, 陈洁, 赵姝, 张燕平. 基于双重最相关注意力网络的协同过滤推荐算法[J]. 计算机应用, 2020, 40(12): 3445-3450.

ZHANG Wenlong, QIAN Fulan, CHEN Jie, ZHAO Shu, ZHANG Yanping. Collaborative filtering recommendation algorithm based on dual most relevant attention network[J]. Journal of Computer Applications, 2020, 40(12): 3445-3450.

参考文献

[1] 黄立威, 江碧涛, 吕守业, 等. 基于深度学习的推荐系统研究综述[J]. 计算机学报, 2018, 41(7):1619-1648.(HUANG L W, JIANG B T,LYU S Y,et al. Survey on deep learning based recommender systems[J]. Chinese Journal of Computers,2018,41(7):1619-1648.)
[2] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 200110th ACM International Conference on World Wide Web. New York:ACM,2001:285-295.
[3] YUAN F,KARATZOGLOU A,ARAPAKIS L,et al. A simple convolutional generativenetwork for next item recommendation[C]//Proceedings of the 201912th ACM International Conference on Web Search and Data Mining. New York:ACM, 2019:582-590.
[4] KOREN Y, BELL R M, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer,2009,42(8):30-37.
[5] WANG H,WANG N,YEUNG D Y. Collaborative deep learning for recommender systems[C]//Proceedings of the 201521th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2015:1235-1244.
[6] HE X,LIAO L,ZHANG H,et al. Neural collaborative filtering[C]//Proceedings of the 201726th International Conference on World Wide Web. New York:ACM,2017:173-182.
[7] BARKAN O, KOENIGSTEIN N. ITEM2VEC:neural item embedding for collaborative filtering[C]//Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing. Piscataway:IEEE,2016:1-6.
[8] ZHENG L,LU C,JIANG F,et al. Spectral collaborative filtering[C]//Proceedings of the 201812th ACM International Conference on Recommender Systems. New York:ACM,2018:311-319.
[9] VASWANI A,SHAZEER N,PARMAR N,et al. Attention is all you need[C]//Proceedings of the 201731st International Conference on Neural Information Processing Systems. Red Hook:Curran Associates Inc.,2017:6000-6010.
[10] KABBUR S, NING X, KARYPIS G. FISM:factored item similarity models for top-n recommender systems[C]//Proceedings of the 201319th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2013:659-667.
[11] NING X,KARYPIS G. SLIM:sparse linear methods for top-n recommender systems[C]//Proceedings of the 201111th IEEE International Conference on Data Mining. Piscataway:IEEE, 2011:497-506.
[12] LECUN Y,BENGIO Y,HINTON G. Deep learning[J]. Nature, 2015,521(7553):436-444.
[13] XUE F,HE X,WANG X,et al. Deep item-based collaborative filtering for top-n recommendation[J]. ACM Transactions on Information Systems,2019,37(3):Article No. 33.
[14] 邓凯, 黄佳进, 秦进. 基于物品的统一推荐模型[J]. 计算机应用, 2020, 40(2):530-534.(DENG K,HUANG J J,QIN J. Itembased unified recommendation model[J]. Journal of Computer Applications,2020,40(2):530-534.)
[15] LUONG T,PHAM H,MANNING C D. Effective approaches to attention-based neural machine translation[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:ACL,2015:1412-1421.
[16] HE X,HE Z,SONG J,et al. NAIS:neural attentive item similarity model for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering,2018,30(12):2354-2366.
[17] CHEN J, ZHANG H, HE X, et al. Attentive collaborative filtering:multimedia recommendation with item-andcomponentlevel attention[C]//Proceedings of the 201740th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM,2017:335-344.
[18] CHEN J,ZHUANG F,HONG X,et al. Attention-driven factor model for explainable personalized recommendation[C]//Proceedings of the 201841th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM,2018:909-912.
[19] GULCEHRE C,CHO K,PASCANU R,et al. Learned-norm pooling for deep feed forward and recurrent neuralnetworks[C]//Proceedings of the 2014 Joint European Conference on Machine Learning and Knowledge Discovery in Databases,LNCS 8724. Berlin:Springer,2014:530-546.
[20] HARPER F M,KONSTAN J A. The movie lens datasets:history and context[J]. ACM Transactions on Interactive Intelligent Systems,2016,5(4):Article No. 19.
[21] FARSEEV A,SAMBORSKII I,FILCHENKOV A,et al. Crossdomain recommendation via clustering on multi-layer graphs[C]//Proceedings of the 201740th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM,2017:195-204.
[22] DENG Z,HUANG L,WANG C,et al. DeepCF:a unified framework of representation learning and matching function learning in recommender system[C]//Proceedings of the 201933rd AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press,2019:61-68.
[23] RENDLE S,FREUDENTHALER C,GANTNER Z,et al. BPR:Bayesian personalized ranking from implicit feedback[C]//Proceedings of the 200925th Conference on Uncertainty in Artificial Intelligence. Arlington:AUAI Press,2009:452-461.

基于双重最相关注意力网络的协同过滤推荐算法

Collaborative filtering recommendation algorithm based on dual most relevant attention network

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