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In this technical survey, we comprehensively summarize the latest advancements in the field of recommender systems.
A. Li, B. Yang, H. Huo, and F. Hussain, “Hypercomplex graph collaborative filtering,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 1914–1922
1922
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R. Agrawal, T. Imieliński, and A. Swami, “Mining association rules between sets of items in large databases,” in Proceedings of the 1993 ACM SIGMOD international conference on Management of data , 1993, pp. 207–216
1993
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T. Wei and J. He, “Comprehensive fair meta-learned recommender system,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2022, pp. 1989–1999
1999
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F. M. Suchanek, G. Kasneci, and G. Weikum, “Yago: a core of semantic knowledge,” in Proceedings of the 16th international conference on World Wide Web , 2007, pp. 697–706
2007
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K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, “Freebase: a collaboratively created graph database for structuring human knowledge,” in Proceedings of the 2008 ACM SIGMOD international conference on Management of data , 2008, pp. 1247–1250
2008
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F. Belleau, M.-A. Nolin, N. Tourigny, P. Rigault, and J. Morissette, “Bio2rdf: towards a mashup to build bioinformatics knowledge systems,” Journal of biomedical informatics , vol. 41, no. 5, pp. 706–716, 2008
2008
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H. J. Ahn, “A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem,” Information Sciences , vol. 178, no. 1, pp. 37–51, 2008
2008
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A. Carlson, J. Betteridge, R. C. Wang, E. R. Hruschka Jr, and T. M. Mitchell, “Coupled semi-supervised learning for information extraction,” in Proceedings of the third ACM international conference on Web search and data mining , 2010, pp. 101–110
2010
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2012
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2013
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A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” in Advances in neural information processing systems , 2013, pp. 2787–2795
2013
Earlier work this paper cites.
H. Steck, “Evaluation of recommendations: rating-prediction and ranking,” in Proceedings of the 7th ACM conference on Recommender systems , 2013, pp. 213–220
2013
Earlier work this paper cites.
Y. Wang, L. Wang, Y. Li, D. He, W. Chen, and T.-Y. Liu, “A theoretical analysis of ndcg ranking measures,” in Proceedings of the 26th annual conference on learning theory (COLT 2013) , vol. 8, 2013, p. 6
2013
Earlier work this paper cites.
D. Vrandečić and M. Krötzsch, “Wikidata: a free collaborative knowledgebase,” Communications of the ACM , vol. 57, no. 10, pp. 78–85, 2014
2014
Earlier work this paper cites.
P. Ernst, C. Meng, A. Siu, and G. Weikum, “Knowlife: a knowledge graph for health and life sciences,” in 2014 IEEE 30th International Conference on Data Engineering . IEEE, 2014, pp. 1254–1257
2014
Earlier work this paper cites.
Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge graph embedding by translating on hyperplanes,” in Twenty-Eighth AAAI conference on artificial intelligence , 2014
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas, P. N. Mendes, S. Hellmann, M. Morsey, P. Van Kleef, S. Auer et al. , “Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia,” Semantic Web , vol. 6, no. 2, pp. 167–195, 2015
2015
Earlier work this paper cites.
Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning entity and relation embeddings for knowledge graph completion,” in Twenty-ninth AAAI conference on artificial intelligence , 2015
2015
Earlier work this paper cites.
G. Ji, S. He, L. Xu, K. Liu, and J. Zhao, “Knowledge graph embedding via dynamic mapping matrix,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , 2015, pp. 687–696
2015
Earlier work this paper cites.
B. Yang, S. W.-t. Yih, X. He, J. Gao, and L. Deng, “Embedding entities and relations for learning and inference in knowledge bases,” in Proceedings of the International Conference on Learning Representations (ICLR) 2015 , 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W.-Y. Ma, “Collaborative knowledge base embedding for recommender systems,” in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining , 2016, pp. 353–362
2016
Earlier work this paper cites.
F. Strub, R. Gaudel, and J. Mary, “Hybrid recommender system based on autoencoders,” in Proceedings of the 1st Workshop on Deep Learning for Recommender Systems , 2016, pp. 11–16
2016
Earlier work this paper cites.
X. Wang, M. Bendersky, D. Metzler, and M. Najork, “Learning to rank with selection bias in personal search,” in Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval , 2016, pp. 115–124
2016
Earlier work this paper cites.
K. Haruna, M. Akmar Ismail, S. Suhendroyono, D. Damiasih, A. C. Pierewan, H. Chiroma, and T. Herawan, “Context-aware recommender system: A review of recent developmental process and future research direction,” Applied Sciences , vol. 7, no. 12, p. 1211, 2017
2017
Earlier work this paper cites.
X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in Proceedings of the 26th international conference on world wide web , 2017, pp. 173–182
2017
Earlier work this paper cites.
L. Zheng, V. Noroozi, and P. S. Yu, “Joint deep modeling of users and items using reviews for recommendation,” in Proceedings of the Tenth ACM International Conference on Web Search and Data Mining , 2017, pp. 425–434
2017
Earlier work this paper cites.
S. Seo, J. Huang, H. Yang, and Y. Liu, “Interpretable convolutional neural networks with dual local and global attention for review rating prediction,” in Proceedings of the Eleventh ACM Conference on Recommender Systems , 2017, pp. 297–305
2017
Earlier work this paper cites.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio et al. , “Graph attention networks,” stat , vol. 1050, no. 20, pp. 10–48 550, 2017
2017
Earlier work this paper cites.
M. Unger, B. Shapira, L. Rokach, and A. Bar, “Inferring contextual preferences using deep auto-encoding,” in Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization , 2017, pp. 221–229
2017
Earlier work this paper cites.
T. Ebesu and Y. Fang, “Neural citation network for context-aware citation recommendation,” in Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval , 2017, pp. 1093–1096
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International conference on machine learning . PMLR, 2017, pp. 1126–1135
2017
Earlier work this paper cites.
P. Li, R. Chen, Q. Liu, J. Xu, and B. Zheng, “Transform cold-start users into warm via fused behaviors in large-scale recommendation,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 2013–2017
2017
Earlier work this paper cites.
J. Y. Chin, K. Zhao, S. Joty, and G. Cong, “Anr: Aspect-based neural recommender,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management , 2018, pp. 147–156
2018
Earlier work this paper cites.
L. Mei, P. Ren, Z. Chen, L. Nie, J. Ma, and J.-Y. Nie, “An attentive interaction network for context-aware recommendations,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management , 2018, pp. 157–166
2018
Earlier work this paper cites.
Y. Jhamb, T. Ebesu, and Y. Fang, “Attentive contextual denoising autoencoder for recommendation,” in Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval , 2018, pp. 27–34
2018
Earlier work this paper cites.
Y. Deldjoo, M. Elahi, M. Quadrana, and P. Cremonesi, “Using visual features based on mpeg-7 and deep learning for movie recommendation,” International journal of multimedia information retrieval , vol. 7, no. 4, pp. 207–219, 2018
2018
Earlier work this paper cites.
Y. Gao, J. Liang, B. Han, M. Yakout, and A. Mohamed, “Building a large-scale, accurate and fresh knowledge graph,” KDD-2018, Tutorial , vol. 39, 2018
2018
Earlier work this paper cites.
H. Wang, F. Zhang, X. Xie, and M. Guo, “Dkn: Deep knowledge-aware network for news recommendation,” in Proceedings of the 2018 world wide web conference , 2018, pp. 1835–1844
2018
Earlier work this paper cites.
J. Huang, W. X. Zhao, H. Dou, J.-R. Wen, and E. Y. Chang, “Improving sequential recommendation with knowledge-enhanced memory networks,” in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval , 2018, pp. 505–514
2018
Earlier work this paper cites.
Z. Sun, J. Yang, J. Zhang, A. Bozzon, L.-K. Huang, and C. Xu, “Recurrent knowledge graph embedding for effective recommendation,” in Proceedings of the 12th ACM Conference on Recommender Systems , 2018, pp. 297–305
2018
Earlier work this paper cites.
Y. Liu, S. Wang, M. S. Khan, and J. He, “A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering,” Big Data Mining and Analytics , vol. 1, no. 3, pp. 211–221, 2018
2018
Earlier work this paper cites.
H. Zhu, Y. Ni, F. Tian, P. Feng, Y. Chen, and Q. Zheng, “A group-oriented recommendation algorithm based on similarities of personal learning generative networks,” IEEE Access , vol. 6, pp. 42 729–42 739, 2018
2018
Earlier work this paper cites.
D. Cao, X. He, L. Miao, Y. An, C. Yang, and R. Hong, “Attentive group recommendation,” in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval , 2018, pp. 645–654
2018
Earlier work this paper cites.
X. He, Z. He, X. Du, and T.-S. Chua, “Adversarial personalized ranking for recommendation,” in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval , 2018, pp. 355–364
2018
Earlier work this paper cites.
Y. Du, M. Fang, J. Yi, C. Xu, J. Cheng, and D. Tao, “Enhancing the robustness of neural collaborative filtering systems under malicious attacks,” IEEE Transactions on Multimedia , vol. 21, no. 3, pp. 555–565, 2018
2018
Earlier work this paper cites.
A. Collins, D. Tkaczyk, A. Aizawa, and J. Beel, “Position bias in recommender systems for digital libraries,” in Transforming Digital Worlds: 13th International Conference, iConference 2018, Sheffield, UK, March 25-28, 2018, Proceedings . Springer, 2018, pp. 335–344
2018
Earlier work this paper cites.
S. Raza and C. Ding, “Progress in context-aware recommender systems—an overview,” Computer Science Review , vol. 31, pp. 84–97, 2019
2019
Earlier work this paper cites.
H. F. Abdulkarem, G. Y. Abozaid, and M. I. Soliman, “Context-aware recommender system frameworks, techniques, and applications: A survey,” in 2019 International Conference on Innovative Trends in Computer Engineering (ITCE) . IEEE, 2019, pp. 180–185
2019
Earlier work this paper cites.
S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: A survey and new perspectives,” ACM computing surveys (CSUR) , vol. 52, no. 1, pp. 1–38, 2019
2019
Earlier work this paper cites.
D. Valcarce, A. Landin, J. Parapar, and Á. Barreiro, “Collaborative filtering embeddings for memory-based recommender systems,” Engineering Applications of Artificial Intelligence , vol. 85, pp. 347–356, 2019
2019
Earlier work this paper cites.
C.-M. Chen, C.-J. Wang, M.-F. Tsai, and Y.-H. Yang, “Collaborative similarity embedding for recommender systems,” in The World Wide Web Conference , 2019, pp. 2637–2643
2019
Earlier work this paper cites.
C. Li, C. Quan, L. Peng, Y. Qi, Y. Deng, and L. Wu, “A capsule network for recommendation and explaining what you like and dislike,” in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval , 2019, pp. 275–284
2019
Earlier work this paper cites.
C. Wu, F. Wu, M. An, J. Huang, Y. Huang, and X. Xie, “Npa: neural news recommendation with personalized attention,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019, pp. 2576–2584
2019
Earlier work this paper cites.
S. Kumar, X. Zhang, and J. Leskovec, “Predicting dynamic embedding trajectory in temporal interaction networks,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , 2019, pp. 1269–1278
2019
Earlier work this paper cites.
X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua, “Neural graph collaborative filtering,” in Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval , 2019, pp. 165–174
2019
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J. Sun, Y. Zhang, C. Ma, M. Coates, H. Guo, R. Tang, and X. He, “Multi-graph convolution collaborative filtering,” in 2019 IEEE international conference on data mining (ICDM) . IEEE, 2019, pp. 1306–1311
2019
Earlier work this paper cites.
M. Unger and A. Tuzhilin, “Hierarchical latent context representation for cars,” IEEE Transactions on Knowledge and Data Engineering , 2019
2019
Earlier work this paper cites.
X. Xin, B. Chen, X. He, D. Wang, Y. Ding, and J. Jose, “Cfm: Convolutional factorization machines for context-aware recommendation.” in IJCAI , vol. 19, 2019, pp. 3926–3932
2019
Earlier work this paper cites.
B. R. Cami, H. Hassanpour, and H. Mashayekhi, “User preferences modeling using dirichlet process mixture model for a content-based recommender system,” Knowledge-Based Systems , vol. 163, pp. 644–655, 2019
2019
Earlier work this paper cites.
C. Wang, T. Zhou, C. Chen, T. Hu, and G. Chen, “Camo: A collaborative ranking method for content based recommendation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 5224–5231
2019
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F. Amato, V. Moscato, A. Picariello, and F. Piccialli, “Sos: a multimedia recommender system for online social networks,” Future generation computer systems , vol. 93, pp. 914–923, 2019
2019
Earlier work this paper cites.
X. Ma, Y. Zhang, and J. Zeng, “Newly published scientific papers recommendation in heterogeneous information networks,” Mobile Networks and Applications , vol. 24, no. 1, pp. 69–79, 2019
2019
Earlier work this paper cites.
Q. Chen, J. Lin, Y. Zhang, M. Ding, Y. Cen, H. Yang, and J. Tang, “Towards knowledge-based recommender dialog system,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , 2019, pp. 1803–1813
2019
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V. Vijayakumar, S. Vairavasundaram, R. Logesh, and A. Sivapathi, “Effective knowledge based recommender system for tailored multiple point of interest recommendation,” International Journal of Web Portals (IJWP) , vol. 11, no. 1, pp. 1–18, 2019
2019
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A. Dadoun, R. Troncy, O. Ratier, and R. Petitti, “Location embeddings for next trip recommendation,” in Companion Proceedings of The 2019 World Wide Web Conference , 2019, pp. 896–903
2019
Earlier work this paper cites.
W. Ma, M. Zhang, Y. Cao, W. Jin, C. Wang, Y. Liu, S. Ma, and X. Ren, “Jointly learning explainable rules for recommendation with knowledge graph,” in The World Wide Web Conference , 2019, pp. 1210–1221
2019
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X. Wang, D. Wang, C. Xu, X. He, Y. Cao, and T.-S. Chua, “Explainable reasoning over knowledge graphs for recommendation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 5329–5336
2019
Cited alongside, same era.
S. Aslan and M. Kaya, “A hybrid recommendation system in co-authorship networks,” in 2019 International Artificial Intelligence and Data Processing Symposium (IDAP) . IEEE, 2019, pp. 1–5
2019
Cited alongside, same era.
A. Gatzioura, J. Vinagre, M. Sànchez-Marrè et al. , “A hybrid recommender system for improving automatic playlist continuation,” IEEE Transactions on Knowledge and Data Engineering , 2019
2019
Cited alongside, same era.
P. Kouki, J. Schaffer, J. Pujara, J. O’Donovan, and L. Getoor, “Personalized explanations for hybrid recommender systems,” in Proceedings of the 24th International Conference on Intelligent User Interfaces , 2019, pp. 379–390
2019
Cited alongside, same era.
C. Wu, D. Lian, Y. Ge, Z. Zhu, and E. Chen, “Triple adversarial learning for influence based poisoning attack in recommender systems,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , 2021, pp. 1830–1840
2021
Later among the works it cites.
V. W. Anelli, A. Bellogin, Y. Deldjoo, T. Di Noia, and F. A. Merra, “Msap: Multi-step adversarial perturbations on recommender systems embeddings,” in The 34th International FLAIRS Conference. The Florida AI Research Society (FLAIRS), AAAI Press , 2021, pp. 1–6
2021
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X. Wang, R. Zhang, Y. Sun, and J. Qi, “Combating selection biases in recommender systems with a few unbiased ratings,” in Proceedings of the 14th ACM International Conference on Web Search and Data Mining , 2021, pp. 427–435
2021
Later among the works it cites.
H. Liu, D. Tang, J. Yang, X. Zhao, J. Tang, and Y. Cheng, “Self-supervised learning for alleviating selection bias in recommendation systems,” Website, 2021
2021
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S. Dara, C. R. Chowdary, and C. Kumar, “A survey on group recommender systems,” Journal of Intelligent Information Systems , pp. 1–25, 2019
2019
Cited alongside, same era.
X. Wang, Y. Liu, J. Lu, F. Xiong, and G. Zhang, “Trugrc: Trust-aware group recommendation with virtual coordinators,” Future Generation Computer Systems , vol. 94, pp. 224–236, 2019
2019
Cited alongside, same era.
H. Yin, Q. Wang, K. Zheng, Z. Li, J. Yang, and X. Zhou, “Social influence-based group representation learning for group recommendation,” in 2019 IEEE 35th International Conference on Data Engineering (ICDE) . IEEE, 2019, pp. 566–577
2019
Cited alongside, same era.
H. Wang, Y. Li, and F. Frimpong, “Group recommendation via self-attention and collaborative metric learning model,” IEEE Access , vol. 7, pp. 164 844–164 855, 2019
2019
Cited alongside, same era.
J. Bai, C. Zhou, J. Song, X. Qu, W. An, Z. Li, and J. Gao, “Personalized bundle list recommendation,” in The World Wide Web Conference , 2019, pp. 60–71
2019
Cited alongside, same era.
J. Tang, X. Du, X. He, F. Yuan, Q. Tian, and T.-S. Chua, “Adversarial training towards robust multimedia recommender system,” IEEE Transactions on Knowledge and Data Engineering , vol. 32, no. 5, pp. 855–867, 2019
2019
Cited alongside, same era.
K. Christakopoulou and A. Banerjee, “Adversarial attacks on an oblivious recommender,” in Proceedings of the 13th ACM Conference on Recommender Systems , 2019, pp. 322–330
2019
Cited alongside, same era.
R. Hu, Y. Guo, M. Pan, and Y. Gong, “Targeted poisoning attacks on social recommender systems,” in 2019 IEEE Global Communications Conference (GLOBECOM) . IEEE, 2019, pp. 1–6
2019
Cited alongside, same era.
Later among the works it cites.
C. Zhou, J. Ma, J. Zhang, J. Zhou, and H. Yang, “Contrastive learning for debiased candidate generation in large-scale recommender systems,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , 2021, pp. 3985–3995
2021
Later among the works it cites.
X. Feng, C. Chen, D. Li, M. Zhao, J. Hao, and J. Wang, “Cmml: Contextual modulation meta learning for cold-start recommendation,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management , 2021, pp. 484–493
2021
Later among the works it cites.
P. Gupta, A. Sharma, P. Malhotra, L. Vig, and G. Shroff, “Causer: Causal session-based recommendations for handling popularity bias,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management , 2021, pp. 3048–3052
2021
Later among the works it cites.
T. Wei, F. Feng, J. Chen, Z. Wu, J. Yi, and X. He, “Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , 2021, pp. 1791–1800
2021
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V. W. Anelli, T. Di Noia, and F. A. Merra, “The idiosyncratic effects of adversarial training on bias in personalized recommendation learning,” in Proceedings of the 15th ACM Conference on Recommender Systems , 2021, pp. 730–735
2021
Later among the works it cites.
L. Ji, Q. Qin, B. Han, and H. Yang, “Reinforcement learning to optimize lifetime value in cold-start recommendation,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management , 2021, pp. 782–791
2021
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J. Wu, X. He, X. Wang, Q. Wang, W. Chen, J. Lian, and X. Xie, “Graph convolution machine for context-aware recommender system,” Frontiers of Computer Science , vol. 16, no. 6, p. 166614, 2022
2022
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H.-w. An and N. Moon, “Design of recommendation system for tourist spot using sentiment analysis based on cnn-lstm,” Journal of Ambient Intelligence and Humanized Computing , pp. 1–11, 2022
2022
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T. Liu, Q. Wu, L. Chang, and T. Gu, “A review of deep learning-based recommender system in e-learning environments,” Artificial Intelligence Review , vol. 55, no. 8, pp. 5953–5980, 2022
2022
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M. Li, X. Zhao, C. Lyu, M. Zhao, R. Wu, and R. Guo, “Mlp4rec: A pure mlp architecture for sequential recommendations,” in 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022) . International Joint Conferences on Artificial Intelligence, 2022, pp. 2138–2144
2022
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W.-S. Chen, Q. Zeng, and B. Pan, “A survey of deep nonnegative matrix factorization,” Neurocomputing , vol. 491, pp. 305–320, 2022
2022
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L. Fang, B. Du, and C. Wu, “Differentially private recommender system with variational autoencoders,” Knowledge-Based Systems , vol. 250, p. 109044, 2022
2022
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Z. Lin, C. Tian, Y. Hou, and W. X. Zhao, “Improving graph collaborative filtering with neighborhood-enriched contrastive learning,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 2320–2329
2022
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L. Xia, C. Huang, Y. Xu, J. Zhao, D. Yin, and J. Huang, “Hypergraph contrastive collaborative filtering,” in Proceedings of the 45th International ACM SIGIR conference on research and development in information retrieval , 2022, pp. 70–79
2022
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M. Zhao, L. Wu, Y. Liang, L. Chen, J. Zhang, Q. Deng, K. Wang, X. Shen, T. Lv, and R. Wu, “Investigating accuracy-novelty performance for graph-based collaborative filtering,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 50–59
2022
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L. Huang, Y. Yang, H. Chen, Y. Zhang, Z. Wang, and L. He, “Context-aware road travel time estimation by coupled tensor decomposition based on trajectory data,” Knowledge-Based Systems , vol. 245, p. 108596, 2022
2022
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Y. Ouyang, P. Wu, and L. Pan, “Asymmetrical context-aware modulation for collaborative filtering recommendation,” in Proceedings of the 31st ACM International Conference on Information & Knowledge Management , 2022, pp. 1595–1604
2022
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D. Nawara and R. Kashef, “Context-aware recommendation systems using consensus-clustering,” in 2022 IEEE International Systems Conference (SysCon) . IEEE, 2022, pp. 1–8
2022
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N. Van Dat, P. Van Toan, and T. M. Thanh, “Solving distribution problems in content-based recommendation system with gaussian mixture model,” Applied Intelligence , vol. 52, no. 2, pp. 1602–1614, 2022
2022
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Y. Yang, Y. Zhu, and Y. Li, “Personalized recommendation with knowledge graph via dual-autoencoder,” Applied Intelligence , pp. 1–12, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
L. Cui and D. Lee, “Ketch: Knowledge graph enhanced thread recommendation in healthcare forums,” in Proceedings of the 45th international acm sigir conference on research and development in information retrieval , 2022, pp. 492–501
2022
Later among the works it cites.
G. Balloccu, L. Boratto, G. Fenu, and M. Marras, “Post processing recommender systems with knowledge graphs for recency, popularity, and diversity of explanations,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 646–656
2022
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B. Hu, Y. Ye, Y. Zhong, J. Pan, and M. Hu, “Transmkr: Translation-based knowledge graph enhanced multi-task point-of-interest recommendation,” Neurocomputing , vol. 474, pp. 107–114, 2022
2022
Later among the works it cites.
Y. Yang, C. Huang, L. Xia, and C. Li, “Knowledge graph contrastive learning for recommendation,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 1434–1443
2022
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S. Geng, Z. Fu, J. Tan, Y. Ge, G. De Melo, and Y. Zhang, “Path language modeling over knowledge graphsfor explainable recommendation,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 946–955
2022
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S.-J. Park, D.-K. Chae, H.-K. Bae, S. Park, and S.-W. Kim, “Reinforcement learning over sentiment-augmented knowledge graphs towards accurate and explainable recommendation,” in Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining , 2022, pp. 784–793
2022
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C. Wu, S. Liu, Z. Zeng, M. Chen, A. Alhudhaif, X. Tang, F. Alenezi, N. Alnaim, and X. Peng, “Knowledge graph-based multi-context-aware recommendation algorithm,” Information Sciences , vol. 595, pp. 179–194, 2022
2022
Later among the works it cites.
J. Zheng, J. Mai, and Y. Wen, “Explainable session-based recommendation with meta-path guided instances and self-attention mechanism,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 2555–2559
2022
Later among the works it cites.
R. Zhao, S. Ju, J. Peng, N. Yang, F. Yan, and S. Sun, “Two-level graph path reasoning for conversational recommendation with user realistic preference,” in Proceedings of the 31st ACM International Conference on Information & Knowledge Management , 2022, pp. 2701–2710
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