A Comprehensive Survey of Recommender Systems Based on Deep Learning
Source abstract
Abstract
With the increasing abundance of information resources and the development of deep learning techniques, recommender systems (RSs) based on deep learning have gradually become a research focus. Although RSs have evolved in recent years, a systematic review of existing RS approaches is still warranted. The main focus of this paper is on recommendation models that incorporate deep learning techniques. The objective is to guide novice researchers interested in this field through the investigation and application of the proposed recommendation models. Specifically, we first categorize existing RS approaches into four types: content-based recommendations, sequence recommendations, cross-domain recommendations, and social recommendation methods. We then introduce the definitions and address the challenges associated with these RS methodologies. Subsequently, we propose a comprehensive categorization framework and novel taxonomies for these methodologies, providing a thorough account of their research advancements. Finally, we discuss future developments regarding this topic.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques · Topic Modeling · Advanced Graph Neural Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China
School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
Funding
Fundamental Research Funds for the Central Universities · National Natural Science Foundation of China · National Key R&D Program of China · Beijing Nova Program from Beijing Municipal Science & Technology Commission
References (100+)
Show source-provided references
1. Recommending learning objects through attentive heterogeneous graph convolution and operation-aware neural network10.1109/TKDE.2021.3125424 ↗
2. Guidelines for the analysis and design of argumentation-based recommendation systems10.1109/MIS.2020.2999569 ↗
3. Eigentaste: A constant time collaborative filtering algorithm10.1023/A:1011419012209 ↗
4. Comprehensive review of artificial neural network applications to pattern recognition10.1109/ACCESS.2019.2945545 ↗
5. A survey on deep learning in big data
6. Covington, P., Adams, J., and Sargin, E. (2016, January 19). Deep neural networks for youtube recommendations. Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA.10.1145/2959100.2959190 ↗
7. Network representation learning: From traditional feature learning to deep learning10.1109/ACCESS.2020.3037118 ↗
8. Han, S., Qiao, Y., Zhang, Y., Lin, W., and Yang, J. (2018, January 15–18). Analyze users’ online shopping behavior using interconnected online interest-product network. Proceedings of the 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain.10.1109/WCNC.2018.8377235 ↗
9. Service Recommendation Based on User Dynamic Preference Extraction and Prediction10.1109/SERVICES.2019.00040 ↗
10. Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey10.1109/ACCESS.2021.3082932 ↗
11. Ouyang, Y., Guo, B., Wang, Q., and Yu, Z. (2021, January 14–23). Cross-domain recommendation with cross-graph knowledge transfer network. Proceedings of the ICC 2021-IEEE International Conference on Communications, Montreal, QC, Canada.10.1109/ICC42927.2021.9500882 ↗
12. Scalable recommendation with social contextual information10.1109/TKDE.2014.2300487 ↗
13. Guo, G. (2012, January 16–20). Resolving data sparsity and cold start in recommender systems. Proceedings of the User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012, Montreal, QC, Canada. Proceedings 20.
14. Social recommendation: A review10.1007/s13278-013-0141-9 ↗
15. An iterative algorithm to derive priority from large-scale sparse pairwise comparison matrix10.1109/TSMC.2021.3049604 ↗
16. Deep learning based recommender system: A survey and new perspectives10.1145/3158369 ↗
17. Region or global a principle for negative sampling in graph-based recommendation
18. Studying the Practices of Logging Exception Stack Traces in Open-Source Software Projects
19. Gantner, Z., Drumond, L., Freudenthaler, C., Rendle, S., and Schmidt-Thieme, L. (2010, January 13–17). Learning attribute-to-feature mappings for cold-start recommendations. Proceedings of the 2010 IEEE International Conference on Data Mining, Sydney, NSW, Australia.10.1109/ICDM.2010.129 ↗
20. Ma, H., Yang, H., Lyu, M.R., and King, I. (2008, January 26–30). Sorec: Social recommendation using probabilistic matrix factorization. Proceedings of the 17th ACM Conference on Information and Knowledge Man-Agement, Napa Valley, CA, USA.10.1145/1458082.1458205 ↗
21. Mnih, A., and Salakhutdinov, R.R. (2007). Advances in Neural Information Processing Systems, Available online: https://proceedings.neurips.cc/paper/2007.
22. Jamali, M., and Ester, M. (2010, January 26–30). A matrix factorization technique with trust propagation for recommendation in social networks. Proceedings of the Fourth ACM Conference on Recommender Systems, Barcelona, Spain.10.1145/1864708.1864736 ↗
23. Ma, H., Zhou, D., Liu, C., Lyu, M.R., and King, I. (2011, January 9–12). Recommender systems with social regularization. Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, Hong Kong, China.10.1145/1935826.1935877 ↗
24. Sedhain, S., Menon, A.K., Sanner, S., and Xie, L. (2015, January 18–22). Autorec: Autoencoders meet collaborative filtering. Proceedings of the 24th Intermnational Conference on World Wide Web, Florence, Italy.10.1145/2740908.2742726 ↗
25. Devooght, R., and Bersini, H. (2017, January 9–12). Long and short-term recommendations with recurrent neural networks. Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, Bratislava, Slovakia.10.1145/3079628.3079670 ↗
26. Wu, S., Sun, F., Zhang, W., Xie, X., and Cui, B. (2020). Graph neural networks in recommender systems: A survey. arXiv.
27. Wang, X., He, X., Wang, M., Feng, F., and Chua, T.S. (2019, January 21–25). Neural graph collaborative filtering. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France.10.1145/3331184.3331267 ↗
28. Hamilton, W., Ying, Z., and Leskovec, J. (2017). Inductive representation learning on large graphs. arXiv.
29. Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., and Leskovec, J. (2018, January 19–23). Graph convolutional neural networks for web-scale recommender systems. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.10.1145/3219819.3219890 ↗
30. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.S. (2017, January 3–7). Neural collaborative filtering. Proceedings of the 26th International Conference on World Wide Web, Perth, Australia.10.1145/3038912.3052569 ↗
31. Guo, H., Tang, R., Ye, Y., Li, Z., and He, X. (2017). DeepFM: A factorization-machine based neural network for CTR prediction. arXiv.10.24963/ijcai.2017/239 ↗
32. FedDeepFM: A Factorization Machine-Based Neural Network for Recommendation in Federated Learning10.1109/ACCESS.2023.3295894 ↗
33. Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L. (2010, January 26–30). Factorizing personalized markov chains for next-basket recommendation. Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, USA.10.1145/1772690.1772773 ↗
34. Kang, W.C., and McAuley, J. (2018, January 17–20). Self-attentive sequential recommendation. Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM), Singapore.10.1109/ICDM.2018.00035 ↗
35. POI recommendation method using LSTM-attention in LBSN considering privacy protection10.1007/s40747-021-00440-8 ↗
36. Lv, F., Jin, T., Yu, C., Sun, F., Lin, Q., Yang, K., and Ng, W. (2019, January 3–7). SDM: Sequential Deep Matching Model for Online Large-scale Recommender System. Proceedings of the CIKM ’19: The 28th ACM International Conference on Information and Knowledge Management, Beijing, China.10.1145/3357384.3357818 ↗
37. Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. (2015). Session-based recommendations with recurrent neural networks. arXiv.
38. CD-SemMF: Cross-Domain Semantic Relatedness Based Matrix Factorization Model Enabled With Linked Open Data for User Cold Start Issue10.1109/ACCESS.2022.3175566 ↗
39. Zhu, F., Chen, C., Wang, Y., Liu, G., and Zheng, X. (2019, January 3–7). Dtcdr: A framework for dual-target cross-domain recommendation. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China.10.1145/3357384.3357992 ↗
40. Li, P., and Tuzhilin, A. (2020, January 3–7). Ddtcdr: Deep dual transfer cross domain recommendation. Proceedings of the 13th International Conference on Web Search and Data Mining, Houston, TX, USA.10.1145/3336191.3371793 ↗
41. Krishnan, A., Das, M., Bendre, M., Yang, H., and Sundaram, H. (2020, January 25–30). Transfer learning via contextual invariants for one-to-many cross-domain recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, China.10.1145/3397271.3401078 ↗
42. He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., and Wang, M. (2020, January 25–30). Lightgcn: Simplifying and powering graph convolution network for recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, China.10.1145/3397271.3401063 ↗
43. Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach
44. Ahmed, A., Shervashidze, N., and Narayanamurthy, S. (2013, January 13–17). Distributed large-scale natural graph factorization. Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil.10.1145/2488388.2488393 ↗
45. Li, Z., Wu, S., Cui, Z., and Zhang, X. (2021). GraphFM: Graph factorization machines for feature interaction modeling. arXiv.
46. Diffnet++: A neural influence and interest diffusion network for social recommendation10.1109/TKDE.2020.3048414 ↗
47. Using collaborative filtering to weave an information tapestry10.1145/138859.138867 ↗
48. Wu, C.Y., Ahmed, A., Beutel, A., Smola, A.J., and Jing, H. (2017, January 6–10). Recurrent recommender networks. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, Cambridge, UK.10.1145/3018661.3018689 ↗
49. Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J. (2013, January 12–16). Efficiency Improvement of Neutrality-Enhanced Recommendation. Proceedings of the Decisions@ RecSys, Hong Kong, China.
50. Rendle, S. (2010, January 13–17). Factorization machines. Proceedings of the 2010 IEEE International Conference on Data Mining, Sydney, NSW, Australia.10.1109/ICDM.2010.127 ↗
51. Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., Yan, Y., Jin, J., Li, H., and Gai, K. (2018, January 19–23). Deep interest network for click-through rate prediction. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.10.1145/3219819.3219823 ↗
52. Deep interest evolution network for click-through rate prediction
53. Li, C., Liu, Z., Wu, M., Xu, Y., Zhao, H., Huang, P., Kang, G., Chen, Q., Li, W., and Lee, D.L. (2019, January 3–7). Multi-interest network with dynamic routing for recommendation at Tmall. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China.10.1145/3357384.3357814 ↗
54. Recommendation systems: An insight into current development and future research challenges10.1109/ACCESS.2022.3194536 ↗
55. De Gemmis, M., Lops, P., Semeraro, G., and Basile, P. (2008, January 23–25). Integrating tags in a semantic content-based recommender. Proceedings of the 2008 ACM Conference on Recommender Systems, Lausanne, Switzerland.10.1145/1454008.1454036 ↗
56. Mooney, R.J., and Roy, L. (2000, January 2–7). Content-based book recommending using learning for text categorization. Proceedings of the Fifth ACM Conference on Digital Libraries, San Antonio, TX, USA.10.1145/336597.336662 ↗
57. A Scientometric Analysis of Research in Recommender Systems10.5530/jscires.5.1.10 ↗
58. Wang, H., Czerminski, R., and Jamieson, A.C. (2021). The Machine Age of Customer Insight, Emerald Publishing Limited.
59. Lu, Y.T., Yu, S.I., Chang, T.C., and Hsu, J.Y.J. (2009, January 11–17). A content-based method to enhance tag recommendation. Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, Pasadena, CA, USA.
60. Peng, J., Zeng, D.D., Zhao, H., and Wang, F.Y. (2010, January 26–30). Collaborative filtering in social tagging systems based on joint item-tag recommendations. Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto, ON, Canada.10.1145/1871437.1871541 ↗
61. Vasile, F., Smirnova, E., and Conneau, A. (2016, January 15–19). Meta-prod2vec: Product embeddings using side-information for recommendation. Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA.10.1145/2959100.2959160 ↗
62. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation
63. Deep learning10.1038/nature14539 ↗
64. Xu, M., Liu, F., and Xu, W. (2019, January 20–22). A survey on sequential recommendation. Proceedings of the 2019 6th International Conference on Information Science and Control Engineering (ICISCE), Shanghai, China.10.1109/ICISCE48695.2019.00031 ↗
65. Collaborative filtering and recommendation algorithm based on matrix factorization and user nearest neighbor model
66. Non-negative matrix factorization algorithm with bias in recommender system
67. Evolution of Deep Learning-Based Sequential Recommender Systems: From Current Trends to New Perspectives10.1109/ACCESS.2023.3281981 ↗
68. Park, K., Lee, J., and Choi, J. (2017, January 6–10). Deep neural networks for news recommendations. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore.10.1145/3132847.3133154 ↗
69. Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv.
70. A survey of knowledge discovery and data mining process models10.1017/S0269888906000737 ↗
71. Villatel, K., Smirnova, E., Mary, J., and Preux, P. (2018). Recurrent neural networks for long and short-term sequential recommendation. arXiv.
72. Exploiting long-and short-term preferences for deep context-aware recommendations10.1109/TCSS.2021.3116059 ↗
73. Long-term IaaS selection using performance discovery10.1109/TSC.2020.3036677 ↗
74. Memory augmented hierarchical attention network for next point-of-interest recommendation10.1109/TCSS.2020.3036661 ↗
75. Ying, H., Zhuang, F., Zhang, F., Liu, Y., Xu, G., Xie, X., Xiong, H., and Wu, J. (2018, January 23–29). Sequential recommender system based on hierarchical attention network. Proceedings of the IJCAI International Joint Conference on Artificial Intelligence, Vienna, Austria.10.24963/ijcai.2018/546 ↗
76. Learning to forget: Continual prediction with LSTM10.1162/089976600300015015 ↗
77. Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv.
78. Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., and Ma, J. (2017, January 6–10). Neural attentive session-based recommendation. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore.10.1145/3132847.3132926 ↗
79. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Advances in Neural Information Processing Systems, Available online: https://proceedings.neurips.cc/paper/2017.
80. Zhang, S., Tay, Y., Yao, L., Sun, A., and An, J. (February, January 27). Next item recommendation with self-attentive metric learning. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.
81. Cho, K., Van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv.10.3115/v1/W14-4012 ↗
82. Tsai, Y.H.H., Bai, S., Liang, P.P., Kolter, J.Z., Morency, L.P., and Salakhutdinov, R. (August, January 28). Multimodal transformer for unaligned multimodal language sequences. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy.
83. Azhir, E., Hosseinzadeh, M., Khan, F., and Mosavi, A. (2022). Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark. Mathematics, 10.10.31219/osf.io/mgpr7 ↗
84. Singer, U., Roitman, H., Eshel, Y., Nus, A., Guy, I., Levi, O., Hasson, I., and Kiperwasser, E. (2022, January 21–25). Sequential modeling with multiple attributes for watchlist recommendation in e-commerce. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, Tempe, AZ, USA.10.1145/3488560.3498453 ↗
85. Tuan, T.X., and Phuong, T.M. (2017, January 27–31). 3D convolutional networks for session-based recommendation with content features. Proceedings of the Eleventh ACM Conference on Recommender Systems, Como, Italy.10.1145/3109859.3109900 ↗
86. Yan, A., Cheng, S., Kang, W.C., Wan, M., and McAuley, J. (2019, January 3–7). CosRec: 2D convolutional neural networks for sequential recommendation. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China.10.1145/3357384.3358113 ↗
87. Tang, J.X., and Wang, K. (2018, January 5–9). Personalized top-n sequential recommendation via convolutional sequence embedding. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Marina Del Rey, CA, USA.10.1145/3159652.3159656 ↗
88. Tan, Y.K., Xu, X., and Liu, Y. (2016, January 15). Improved recurrent neural networks for session-based recommendations. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, MA, USA.10.1145/2988450.2988452 ↗
89. Bogina, V., and Kuflik, T. (2017, January 27–31). Incorporating Dwell Time in Session-Based Recommendations with Recurrent Neural Networks. Proceedings of the RecTemp@ RecSys, Como, Italy.
90. Hidasi, B., and Karatzoglou, A. (2018, January 22–26). Recurrent neural networks with top-k gains for session-based recommendations. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy.10.1145/3269206.3271761 ↗
91. Hidasi, B., Quadrana, M., Karatzoglou, A., and Tikk, D. (2016, January 15–19). Parallel recurrent neural network architectures for feature-rich session-based recommendations. Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA.10.1145/2959100.2959167 ↗
92. Jannach, D., and Ludewig, M. (2017, January 27–31). When recurrent neural networks meet the neighborhood for session-based recommendation. Proceedings of the Eleventh ACM Conference on Recommender Systems, Como, Italy.10.1145/3109859.3109872 ↗
93. Chen, X., Xu, H., Zhang, Y., Tang, J., Cao, Y., Qin, Z., and Zha, H. (2018, January 5–9). Sequential recommendation with user memory networks. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA.10.1145/3159652.3159668 ↗
94. Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., and Jiang, P. (2019, January 3–7). BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China.10.1145/3357384.3357895 ↗
95. Dynamic Multi-Objective Optimization Framework with Interactive Evolution for Sequential Recommendation10.1109/TETCI.2023.3251352 ↗
96. Cen, Y., Zhang, J., Zou, X., Zhou, C., Yang, H., and Tang, J. (2020, January 6–10). Controllable multi-interest framework for recommendation. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, CA, USA.10.1145/3394486.3403344 ↗
97. Meng, W., Yang, D., and Xiao, Y. (2020, January 25–30). Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, China.10.1145/3397271.3401098 ↗
98. Enhanced Attention Framework for Multi-Interest Sequential Recommendation10.1109/ACCESS.2022.3185063 ↗
99. Huang, X., Qian, S., Fang, Q., Sang, J., and Xu, C. (2018, January 22–26). Csan: Contextual self-attention network for user sequential recommendation. Proceedings of the 26th ACM International Conference on Multimedia, Seoul, Republic of Korea.10.1145/3240508.3240609 ↗
100. Automatic ontology-based user profile learning from heterogeneous web resources in a big data context10.14778/2536274.2536330 ↗