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This survey paper conducts a comprehensive analysis of the evolution and contemporary landscape of recommendation systems, which have been extensively incorporated across a myriad of web applications.
M. Cheng, H. Gao, Data-driven life-cycle risk assessment of bridge networks using bayesian network, in: Life-Cycle of Structures and Infrastructure Systems, CRC Press, 2023, pp. 1953–1960
1960
Earlier work this paper cites.
D. W. Bates, J. M. Teich, J. Lee, D. Seger, G. J. Kuperman, N. Ma’Luf, D. Boyle, L. Leape, The impact of computerized physician order entry on medication error prevention, Journal of the American Medical Informatics Association 10 (4) (2003) 321–327
2003
Earlier work this paper cites.
G. Adomavicius, A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE transactions on knowledge and data engineering 17 (6) (2005) 734–749
2005
Earlier work this paper cites.
A. Coulter, J. Ellins, Effectiveness of strategies for informing, educating, and involving patients, BMJ 335 (7609) (2007) 24–27
2007
Earlier work this paper cites.
M. Ge, C. Delgado-Battenfeld, D. Jannach, The long tail of recommender systems and how to leverage it, Proceedings of the 1st ACM conference on Recommender systems (2007) 11–18
2007
Earlier work this paper cites.
K. G. Shojania, A. Jennings, A. Mayhew, C. R. Ramsay, M. P. Eccles, J. M. Grimshaw, The effects of on-screen, point of care computer reminders on processes and outcomes of care, Cochrane Database of Systematic Reviews (3) (2009)
2009
Earlier work this paper cites.
R. L. Street Jr, G. Makoul, N. K. Arora, R. M. Epstein, How does communication heal? pathways linking clinician–patient communication to health outcomes, Patient Education and Counseling 74 (3) (2009) 295–301
2009
Earlier work this paper cites.
S. C. Brailsford, J. Vissers, H. Buxton, A comprehensive review of patient simulation in nursing education, Simulation & Gaming 40 (4) (2009) 529–565
2009
Earlier work this paper cites.
S. J. Pan, Q. Yang, Transfer learning for collaborative filtering, in: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, ACM, 2009, pp. 101–108
2009
Earlier work this paper cites.
P. Lops, M. d. Gemmis, G. Semeraro, Content-based recommender systems: State of the art and trends, Recommender systems handbook (2011) 73–105
2011
Earlier work this paper cites.
Y.-L. Lee, F.-H. Huang, Recommender system architecture for adaptive green marketing, Expert Systems with Applications 38 (8) (2011) 9696–9703
2011
Earlier work this paper cites.
G. Adomavicius, A. Tuzhilin, Context-aware recommender systems, AI magazine 32 (3) (2011) 67–80
2011
Earlier work this paper cites.
S. Shishehchi, S. Y. Banihashem, N. A. M. Zin, S. A. M. Noah, K. Malaysia, Ontological approach in knowledge based recommender system to develop the quality of e-learning system, Australian Journal of Basic and Applied Sciences 6 (2) (2012) 115–123
2012
Earlier work this paper cites.
M. T. Ribeiro, A. Lacerda, A. Veloso, N. Ziviani, Pareto-efficient hybridization for multi-objective recommender systems, in: Proceedings of the sixth ACM conference on Recommender systems, 2012, pp. 19–26
2012
Earlier work this paper cites.
X. Wu, X. Zhu, G.-Q. Wu, W. Ding, Data mining with big data, IEEE transactions on knowledge and data engineering 26 (1) (2013) 97–107
2013
Earlier work this paper cites.
C. K. Emani, N. Cullot, C. Nicolle, Understandable big data: a survey, Computer science review 17 (2015) 70–81
2015
Earlier work this paper cites.
J. P. Verma, B. Patel, A. Patel, Big data analysis: recommendation system with hadoop framework, in: 2015 IEEE International Conference on Computational Intelligence & Communication Technology, IEEE, 2015, pp. 92–97
2015
Earlier work this paper cites.
G. Chaithra, et al., User preferences based recommendation system for services using mapreduce approach (2015)
2015
Earlier work this paper cites.
M. Elahi, F. Ricci, N. Rubens, A survey of active learning in collaborative filtering recommender systems, Computer Science Review 20 (2016) 29–50
2016
Earlier work this paper cites.
B. Alhijawi, Y. Kilani, Using genetic algorithms for measuring the similarity values between users in collaborative filtering recommender systems, in: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), IEEE, 2016, pp. 1–6
2016
Earlier work this paper cites.
F. Zhang, T. Gong, V. E. Lee, G. Zhao, C. Rong, G. Qu, Fast algorithms to evaluate collaborative filtering recommender systems, Knowledge-Based Systems 96 (2016) 96–103
2016
Earlier work this paper cites.
C. Musto, G. Semeraro, M. d. Gemmis, P. Lops, Learning word embeddings from wikipedia for content-based recommender systems, in: European conference on information retrieval, Springer, 2016, pp. 729–734
2016
Earlier work this paper cites.
C. C. Aggarwal, Knowledge-based recommender systems, in: Recommender systems, Springer, 2016, pp. 167–197
2016
Earlier work this paper cites.
M. Hassan, M. Hamada, Enhancing learning objects recommendation using multi-criteria recommender systems, in: 2016 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), IEEE, 2016, pp. 62–64
2016
Earlier work this paper cites.
Y. Zhang, X. Liu, W. Liu, C. Zhu, Hybrid recommender system using semi-supervised clustering based on gaussian mixture model, in: 2016 international conference on cyberworlds (CW), IEEE, 2016, pp. 155–158
2016
Earlier work this paper cites.
J. D. West, I. Wesley-Smith, C. T. Bergstrom, A recommendation system based on hierarchical clustering of an article-level citation network, IEEE Transactions on Big Data 2 (2) (2016) 113–123
2016
Earlier work this paper cites.
S. D. Kadam, D. Motwani, S. A. Vaidya, Big data analytics-recommendation system with hadoop framework, in: 2016 International Conference on Inventive Computation Technologies (ICICT), Vol. 3, IEEE, 2016, pp. 1–5
2016
Earlier work this paper cites.
D. P. Acharjya, K. Ahmed, A survey on big data analytics: challenges, open research issues and tools, International Journal of Advanced Computer Science and Applications 7 (2) (2016) 511–518
2016
Earlier work this paper cites.
A. V. Dev, A. Mohan, Recommendation system for big data applications based on set similarity of user preferences, in: 2016 International Conference on Next Generation Intelligent Systems (ICNGIS), IEEE, 2016, pp. 1–6
2016
Earlier work this paper cites.
E. R. Dorsey, E. J. Topol, State of telehealth, New England Journal of Medicine 375 (2) (2016) 154–161
2016
Earlier work this paper cites.
Y. Zheng, B. Tang, W. Ding, H. Zhou, A neural autoregressive approach to collaborative filtering, in: International Conference on Machine Learning, PMLR, 2016, pp. 764–773
2016
Earlier work this paper cites.
doi:10.1109/ACCESS.2017.2718038
F. Ali, D. Kwak, P. Khan, S. H. A. Ei-Sappagh, S. M. R. Islam, D. Park, K.-S. Kwak, Merged ontology and svm-based information extraction and recommendation system for social robots, IEEE Access 5 (2017) 12364–12379 · 2017
Earlier work this paper cites.
M. Volkovs, G. W. Yu, T. Poutanen, Content-based neighbor models for cold start in recommender systems, in: Proceedings of the Recommender Systems Challenge 2017, 2017, pp. 1–6
2017
Earlier work this paper cites.
R. Cabezas, J. G. Ruizº, M. Leyva, A knowledge-based recommendation framework using svn, Neutrosophic Sets and Systems 16 (2017) 24
2017
Earlier work this paper cites.
X. Li, H. Wang, A survey on resource allocation in cloud computing: Issues and solutions, Journal of Network and Computer Applications 80 (2017) 90–106
2017
Earlier work this paper cites.
T. Numnonda, A real-time recommendation engine using lambda architecture, Artificial Life and Robotics 23 (2) (2018) 249–254
2018
Earlier work this paper cites.
J. Xiao, M. Wang, B. Jiang, J. Li, A personalized recommendation system with combinational algorithm for online learning, Journal of Ambient Intelligence and Humanized Computing 9 (3) (2018) 667–677
2018
Earlier work this paper cites.
H. Zhang, T. Huang, Z. Lv, S. Liu, Z. Zhou, Mcrs: A course recommendation system for moocs, Multimedia Tools and Applications 77 (6) (2018) 7051–7069
2018
Earlier work this paper cites.
L. Al Hassanieh, C. Abou Jaoudeh, J. B. Abdo, J. Demerjian, Similarity measures for collaborative filtering recommender systems, in: 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM), IEEE, 2018, pp. 1–5
2018
Earlier work this paper cites.
J. K. Tarus, Z. Niu, G. Mustafa, Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning, Artificial intelligence review 50 (1) (2018) 21–48
2018
Earlier work this paper cites.
X. Zhou, W. Liang, I. Kevin, K. Wang, R. Huang, Q. Jin, Academic influence aware and multidimensional network analysis for research collaboration navigation based on scholarly big data, IEEE Transactions on Emerging Topics in Computing 9 (1) (2018) 246–257
2018
Earlier work this paper cites.
P. Ram Mohan Rao, S. Murali Krishna, A. Siva Kumar, Privacy preservation techniques in big data analytics: a survey, Journal of Big Data 5 (1) (2018) 1–12
2018
Cited alongside, same era.
J. Chen, K. Li, H. Rong, K. Bilal, N. Yang, K. Li, A disease diagnosis and treatment recommendation system based on big data mining and cloud computing, Information Sciences 435 (2018) 124–149
2018
Cited alongside, same era.
Y.-w. Zhang, Y.-y. Zhou, F.-t. Wang, Z. Sun, Q. He, Service recommendation based on quotient space granularity analysis and covering algorithm on spark, Knowledge-Based Systems 147 (2018) 25–35
2018
Cited alongside, same era.
B. J. Gogoi, Green building features and factors affecting the consumer choice for green building recommendation, International Journal of Civil Engineering & Technology 9 (6) (2018) 127–136
2018
Cited alongside, same era.
J. Veselka, M. Nehasilová, K. Dvořáková, P. Ryklová, M. Volf, J. Ružička, A. Lupíšek, Recommendations for developing a bim for the purpose of lca in green building certifications, Sustainability 12 (15) (2020) 6151
2020
Later among the works it cites.
Y. Leng, R. Ruiz, X. Dong, A. Pentland, Interpretable recommender system with heterogeneous information: A geometric deep learning perspective, SSRN Electron. J 10 (2020) 2411–2430
2020
Later among the works it cites.
I. Shenbin, A. Alekseev, E. Tutubalina, V. Malykh, S. I. Nikolenko, Recvae: A new variational autoencoder for top-n recommendations with implicit feedback, in: Proceedings of the 13th international conference on web search and data mining, 2020, pp. 528–536
2020
Later among the works it cites.
F. Zhou, B. Luo, T. Hu, Z. Chen, Y. Wen, A combinatorial recommendation system framework based on deep reinforcement learning, in: 2021 IEEE International Conference on Big Data (Big Data), IEEE, 2021, pp. 5733–5740
2021
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F. Fatehi, R. Wootton, A clinician’s guide to the use of artificial intelligence in telemedicine–a focus on triage services, Studies in Health Technology and Informatics 252 (2018) 46–51
2018
Cited alongside, same era.
W. S. Choi, M. J. Rho, A systematic review of research trends and future directions in gamification, in: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, ACM, 2018, p. LBW511
2018
Cited alongside, same era.
L. Muller, J. Martel, G. Indiveri, Kernelized synaptic weight matrices, in: International Conference on Machine Learning, PMLR, 2018, pp. 3654–3663
2018
Cited alongside, same era.
M. Zehlike, C. Castillo, M. Lalmas, Fairness-aware recommendation in social networks, ACM Transactions on Information Systems (TOIS) 36 (3) (2018) 1–33
2018
Cited alongside, same era.
Z. Huang, X. Xu, J. Ni, H. Zhu, C. Wang, Multimodal representation learning for recommendation in internet of things, IEEE Internet of Things Journal 6 (6) (2019) 10675–10685
2019
Cited alongside, same era.
J.-H. Huh, T.-J. Kim, A location-based mobile health care facility search system for senior citizens, The Journal of Supercomputing 75 (2019) 1831–1848
2019
Cited alongside, same era.
B. Yi, X. Shen, H. Liu, Z. Zhang, W. Zhang, S. Liu, N. Xiong, Deep matrix factorization with implicit feedback embedding for recommendation system, IEEE Transactions on Industrial Informatics 15 (8) (2019) 4591–4601
2019
Cited alongside, same era.
F. Zhang, V. E. Lee, R. Jin, S. Garg, K.-K. R. Choo, M. Maasberg, L. Dong, C. Cheng, Privacy-aware smart city: A case study in collaborative filtering recommender systems, Journal of Parallel and Distributed Computing 127 (2019) 145–159
2019
Cited alongside, same era.
Later among the works it cites.
N. Entezari, E. E. Papalexakis, H. Wang, S. Rao, S. K. Prasad, Tensor-based complementary product recommendation, in: 2021 IEEE International Conference on Big Data (Big Data), IEEE, 2021, pp. 409–415
2021
Later among the works it cites.
B. Li, A. Maalla, M. Liang, Research on recommendation algorithm based on e-commerce user behavior sequence, in: 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Vol. 2, IEEE, 2021, pp. 914–918
2021
Later among the works it cites.
X. Li, F. Sun, Sports training recommendation method under the background of data analysis, in: 2021 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), IEEE, 2021, pp. 12–16
2021
Later among the works it cites.
Y. Pérez-Almaguer, R. Yera, A. A. Alzahrani, L. Martínez, Content-based group recommender systems: A general taxonomy and further improvements, Expert Systems with Applications 184 (2021) 115444
2021
Later among the works it cites.
F. Cena, L. Console, F. Vernero, Logical foundations of knowledge-based recommender systems: A unifying spectrum of alternatives, Information Sciences 546 (2021) 60–73
2021
Later among the works it cites.
A. Zagranovskaia, D. Mitura, Designing hybrid recommender systems, in: IV International Scientific and Practical Conference, 2021, pp. 1–5
2021
Later among the works it cites.
A. J. Ibrahim, P. Zira, N. Abdulganiyyi, Hybrid recommender for research papers and articles, International Journal of Intelligent Information Systems 10 (2) (2021) 9
2021
Later among the works it cites.
X. He, X. Ke, Research summary of recommendation system based on knowledge graph, in: The 2021 3rd International Conference on Big Data Engineering, 2021, pp. 104–109
2021
Later among the works it cites.
H. Chen, A dqn-based recommender system for item-list recommendation, in: 2021 IEEE International Conference on Big Data (Big Data), IEEE, 2021, pp. 5699–5702
2021
Later among the works it cites.
K. Al Fararni, F. Nafis, B. Aghoutane, A. Yahyaouy, J. Riffi, A. Sabri, Hybrid recommender system for tourism based on big data and ai: A conceptual framework, Big Data Mining and Analytics 4 (1) (2021) 47–55
2021
Later among the works it cites.
Z. Wan, Research on e-commerce recommendation system based on big data technology, in: Journal of Physics: Conference Series, Vol. 1883, IOP Publishing, 2021, p. 012159
2021
Later among the works it cites.
M. Uzun-Per, A. B. Can, A. V. Gürel, M. S. Aktaş, Big data testing framework for recommendation systems in e-science and e-commerce domains, in: 2021 IEEE International Conference on Big Data (Big Data), IEEE, 2021, pp. 2353–2361
2021
Later among the works it cites.
J. Xu, Sustainable technology & social ecology with architectural design (2021)
2021
Later among the works it cites.
Y. Himeur, A. Alsalemi, A. Al-Kababji, F. Bensaali, A. Amira, C. Sardianos, G. Dimitrakopoulos, I. Varlamis, A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects, Information Fusion 72 (2021) 1–21
2021
Later among the works it cites.
S. C. Han, T. Lim, S. Long, B. Burgstaller, J. Poon, Glocal-k: Global and local kernels for recommender systems, in: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021, pp. 3063–3067
2021
Later among the works it cites.
V. Vančura, P. Kordík, Deep variational autoencoder with shallow parallel path for top-n recommendation (vasp), in: International Conference on Artificial Neural Networks, Springer, 2021, pp. 138–149
2021
Later among the works it cites.
K. Mao, J. Zhu, X. Xiao, B. Lu, Z. Wang, X. He, Ultragcn: ultra simplification of graph convolutional networks for recommendation, in: Proceedings of the 30th ACM international conference on information & knowledge management, 2021, pp. 1253–1262
2021
Later among the works it cites.
Y. Zhanga, Z. Zenga, G. Taob, Z. Shena, Towards sustainable living via green recommendation systems, International Journal of Information Technology 28 (1) (2022)
2022
Closest in time.
Z. Z. Darban, M. H. Valipour, Ghrs: Graph-based hybrid recommendation system with application to movie recommendation, Expert Systems with Applications 200 (2022) 116850
2022
Closest in time.
Y. Du, X. Zhu, L. Chen, B. Zheng, Y. Gao, Hakg: Hierarchy-aware knowledge gated network for recommendation, in: Proceedings of the 45th international ACM SIGIR conference on Research and development in Information Retrieval, 2022, pp. 1390–1400
2022
Closest in time.
2023
Closest in time.
A. Felfernig, M. Wundara, T. N. T. Tran, S. Polat-Erdeniz, S. Lubos, M. El Mansi, D. Garber, V.-M. Le, Recommender systems for sustainability: overview and research issues, Frontiers in big Data 6 (2023)
2023
Closest in time.
M. T. Siddique, P. Koukaras, D. Ioannidis, C. Tjortjis, Smartbuild recsys: A recommendation system based on the smart readiness indicator for energy efficiency in buildings, Algorithms 16 (10) (2023) 482
2023
Closest in time.
J. Domingo Gil, Recommender system for sustainable alternatives of building materials: sustainability-focused decision making in the construction industry (2023)
2023
Closest in time.
G. Albora, L. R. Mori, A. Zaccaria, Sapling similarity: A performing and interpretable memory-based tool for recommendation, Knowledge-Based Systems 275 (2023) 110659
2023
Closest in time.
M. Spišák, R. Bartyzal, A. Hoskovec, L. Peska, M. Tuma, Scalable approximate nonsymmetric autoencoder for collaborative filtering, in: Proceedings of the 17th ACM Conference on Recommender Systems, 2023, pp. 763–770
2023
Closest in time.
J. Choi, S. Hong, N. Park, S.-B. Cho, Blurring-sharpening process models for collaborative filtering, in: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023, pp. 1096–1106
2023
Closest in time.
J. Choi, S. Hong, N. Park, S.-B. Cho, Blurring-sharpening process models for collaborative filtering, in: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023, pp. 1096–1106
2023
Closest in time.
2024
Closest in time.
J. Hu, B. Hooi, S. Qian, Q. Fang, C. Xu, Mgdcf: Distance learning via markov graph diffusion for neural collaborative filtering, IEEE Transactions on Knowledge and Data Engineering (2024)
2024
Closest in time.
MovieLens latest datasets, https://grouplens.org/datasets/movielens/latest/ , accessed: 2024-03-03
2024
Closest in time.
Amazon Customer Reviews Dataset, https://registry.opendata.aws/amazon-reviews/ , accessed: 2024-03-03
2024
Closest in time.
Netflix Prize Dataset, https://www.netflixprize.com/ , accessed: 2024-03-03
2024
Closest in time.
Last.fm Dataset, https://grouplens.org/datasets/hetrec-2011/ , accessed: 2024-03-03
2024
Closest in time.
Yelp Dataset, https://www.yelp.com/dataset , accessed: 2024-03-03
2024
Closest in time.
F. Rezaimehr, C. Dadkhah, A survey of attack detection approaches in collaborative filtering recommender systems, Artificial Intelligence Review 54 (3) (2021) 2011–2066
2066
Closest in time.