2021 04 v.36;No.156 325-336
基于深度神经网络的推荐系统研究综述
基金项目(Foundation):
国家自然科学基金资助项目(61772321);;
山东省自然科学基金重点项目(ZR202011020044)
邮箱(Email):
DOI:
中文作者单位:
山东师范大学信息科学与工程学院;山东女子学院数据科学与计算机学院;
摘要(Abstract):
在信息过载时代,从海量信息中寻找感兴趣的信息是一件非常困难的事.推荐系统可以从大数据中挖掘用户的偏好信息,从而向用户提供精确的个性化推荐服务.近年来,深度神经网络在推荐系统中得到了广泛的应用,具有独特的特征提取能力.本文对推荐系统进行梳理,在讨论传统推荐算法的基础上,综述了基于深度神经网络的推荐系统的研究进展,分析了与传统推荐方法的区别与优势,归纳了推荐系统的性能评价指标.介绍了所提出的三个基于深度学习的推荐模型.并对推荐系统的未来发展趋势进行展望.
关键词(KeyWords):
推荐系统;;深度神经网络;;深度学习;;评价指标;;注意力机制
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参考文献
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[ 2 ] Francesco R,Lior R,Bracha S.Introduction to Recommender Systems Handbook[M].Recommender Systems Handbook.Springer,2011:1-35.
[ 3 ] Raza S,Ding C.Progress in context-aware recommender systems-An overview[J].Computer Science Review,2019,31:84-97.
[ 4 ] Michael D E,John T R,Joseph A K,et al.Collaborative filtering recommender systems[J].Human-Computer Interaction,2011,4(2):81–173.
[ 5 ] Jonathan L H,Joseph A K,John R.Explaining collaborative filtering recommendations[C].ACM Conference on Computer Supported Cooperative Work,2011:241–250.
[ 6 ] Pan Y,He F,Yu H.A novel enhanced collaborative autoencoder with knowledge distillation for top-N recommender systems[J].Neurocomputing,2019,332:137-148.
[ 7 ] Fang H,Guo G,Zhang D,et al.Deep learning-based sequential recommender systems:concepts,algorithms,and evaluations[C].International Conference on Web Engineering,Springer,Cham,2019:574-577.
[ 8 ] Yehuda K.Factorization meets the neighborhood:a multifaceted collaborative filtering model[C].In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2008:426-434.
[ 9 ] Yehuda K,Robert B,Chris V.Matrix factorization techniques for recommender systems[J].Computer,2009,42(8):30-37.
[10] Yi B,Shen X,Liu H,et al.Deep matrix factorization with implicit feedback embedding for recommendation system[J].IEEE Transactions on Industrial Informatics,2019,15(8):4591-4601.
[11] Salakhutdinov R,Mnih A,Hinton G.Restricted Boltzmann machines for collaborative filtering[C].Proceedings of the 24th International Conference on Machine Learning,2007:791-798.
[12] 王科强.基于矩阵分解的个性化推荐系统[D].上海:华东师范大学,2017.
[13] Wang H,Zhao M,Xie X,et al.Knowledge graph convolutional networks for recommender systems[C].The World Wide web Conference,2019:3307-3313.
[14] Milano S,Taddeo M,Floridi L.Recommender systems and their ethical challenges[J].AI & SOCIETY,2020:1-11.
[15] Varga E.Recommender Systems[M].Practical Data Science with Python 3.Apress,Berkeley,CA,2019:317-339.
[16] Coates A,Ng A,Lee H.An analysis of single-layer networks in unsupervised feature learning[C].Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics,2011:215-223.
[17] Salakhutdinov R,Hinton G E.Deep Boltzmann machines[C].In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics,2009:448-455.
[18] Georgiev K,Nakov P.A non-iid framework for collaborative filtering with restricted Boltzmann machines[C].International Conference on Machine Learning,2013:1148-1156.
[19] Wang J,Ding K,Hong L,et al.Next-item recommendation with sequential hypergraphs[C].Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval,2020:1101-1110.
[20] Chen T,Yin H,Chen H,et al.Air:Attentional intention-aware recommender systems[C].2019 IEEE 35th International Conference on Data Engineering (ICDE),IEEE,2019:304-315.
[21] Cui Z,Xu X,Fei X U E,et al.Personalized recommendation system based on collaborative filtering for IoT scenarios[J].IEEE Transactions on Services Computing,2020,13(4):685-695.
[22] Wu Y,DuBois C,Zheng A X,et al.Collaborative denoising auto-encoders for top-n recommender systems[C].Proceedings of the Ninth ACM International Conference on Web Search and Data Mining,2016:153-162.
[23] Li S,Kawale J,Fu Y.Deep collaborative filtering via marginalized denoising auto-encoder[C].Proceedings of the 24th ACM International on Conference on Information and Knowledge Management,2015:811-820.
[24] Wang H,Shi X,Yeung D Y.Relational stacked denoising autoencoder for tag recommendation[C].The Associationfor the Advance of Artificial Intelligence,2015:3052-3058.
[25] Strub F,Gaudel R,Mary J.Hybrid recommender system based on autoencoders[C].Proceedings of the 1st Workshop on Deep Learning for Recommender Systems,2016:11-16.
[26] 张敏,丁弼原,马为之.基于深度学习加强的混合推荐方法[J].清华大学学报:自然科学版,2017,(10):9-16.
[27] You J,Wang Y,Pal A,et al.Hierarchical temporal convolutional networks for dynamic recommender systems[C].The World Wide Web Conference,2019:2236-2246.
[28] Chung J,Gulcehre C,Cho K H,et al.Empirical evaluation of gated recurrent neural networks on sequence modeling[J].2014:arXiv preprint arXiv:1412.3555.
[29] Cui Z,Xu X,Xue F,et al.Personalized recommendation system based on collaborative filtering for IoT scenarios[J].IEEE Transactions on Services Computing,2020.
[30] Zheng L,Noroozi V,Yu P S.Joint deep modeling of users and items using reviews for recommendation[C].Proceedings of the Tenth ACM International Conference on Web Search and Data Mining,ACM,2017:425-434.
[31] Zhou J,Albatal R,Gurrin C.Applying visual user interest profiles for recommendation and personalisation[C].International Conference on Multimedia Modeling,2016:361-366.
[32] Huang Z,Xu X,Zhu H,et al.An efficient group recommendation model with multiattention-based neural networks[J].IEEE Transactions on Neural Networks and Learning Systems,2020,31(11):4461-4474.
[33] Naumov M,Mudigere D,Shi H J M,et al.Deep learning recommendation model for personalization and recommendation systems[J].2019:eprint arXiv:1906.00091.
[34] Seo S,Huang J,Yang H,et al.Interpretable convolutional neural networks with dual local and global attention for review rating prediction[C].Proceedings of the Eleventh ACM Conference on Recommender Systems,2017:297-305.
[35] Zhang F,Yuan N J,Lian D,et al.Collaborative knowledge base embedding for recommender systems[C].Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,ser.KDD ′16.New York,NY,USA:ACM,2016:353-362
[36] Guo Q,Zhuang F,Qin C,et al.A survey on knowledge graph-based recommender systems[J].IEEE Transactions on Knowledge and Data Engineering,2020.
[37] Wang X,He X,Cao Y,et al.Kgat:Knowledge graph attention network for recommendation[C].Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,2019:950-958.
[38] Zhou K,Zhao W X,Bian S,et al.Improving conversational recommender systems via knowledge graph based semantic fusion[C].Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,2020:1006-1014.
[39] Zhou S,Dai X,Chen H,et al.Interactive recommender system via knowledge graph-enhanced reinforcement learning[C].Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval,2020:179-188.
[40] Palumbo E,Monti D,Rizzo G,et al.entity2rec:Property-specific knowledge graph embeddings for item recommendation[J].Expert Systems with Applications,2020,151:113235.
[41] 黄立威,江碧涛,吕守业.基于深度学习的推荐系统研究综述[J],计算机学报,2018,427(07):191-219.
[42] Yin R,Li K,Zhang G,et al.A deeper graph neural network for recommender systems[J].Knowledge-Based Systems,2019,185:105020.
[43] 张岐山,翁丽娟.社会化推荐系统综述[J].计算机工程与应用,2020,56(1):1-10.
[44] Chen W,Cai F,Chen H,et al.Joint neural collaborative filtering for recommender systems[J].ACM Transactions on Information Systems (TOIS),2019,37(4):1-30.
[45] Swets J A.Information retrieval systems[J].Journal of Family Practice,1963,141(3577):245-250.
[46] Breese J S,Heckerman D,Kadie C.Empirical analysis of predictive algorithms for collaborative filtering[J].Uncertainty in Artificial Intelligence,2013,98(7):43-52.
[47] Tan Q,Liu F.Recommendation based on users′long-term and short-term interests with attention[J].Mathematical Problems in Engineering,2019.
[48] Xing S,Liu,F A,Wang Q,et al.Content-aware point-of-interest recommendation based on convolutional neural network[J].Applied Intelligence,2018.
[49] Xing S,Liu F,Wang Q,et al.A hierarchical attention model for rating prediction by leveraging user and product reviews[J].Neurocomputing,2019,332:417-427.
[50] Zhou Z Y,Liu F A,Filter gate network based on multi-head attention for aspect-level sentiment classification[J].Neurocomputing,2021,441:214-225.
基本信息:
DOI:
中图分类号:TP391.3;TP183
引用信息:
[1]刘方爱,王倩倩,郝建华.基于深度神经网络的推荐系统研究综述[J].山东师范大学学报(自然科学版),2021,36(04):325-336.
基金信息:
国家自然科学基金资助项目(61772321);; 山东省自然科学基金重点项目(ZR202011020044)
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