Computer Science ›› 2023, Vol. 50 ›› Issue (9): 152-159.doi: 10.11896/jsjkx.220900035
• Database & Big Data & Data Science • Previous Articles Next Articles
HUANG Lu, NI Lyu, JIN Cheqing
CLC Number:
[1]CHEN J W,WANG X,FENG F L,et al.Bias issues and solutions in recommender system:Tutorial on the RecSys 2021[C]//Proceedings of the 15th ACM Conference on Recommender Systems.Amsterdam,New York:ACM,2021:825-827. [2]SCHNABELT,SWAMINATHAN A,SINGH A,et al.Recommendations as treatments:debiasing learning and evaluation[C]//Proceedings of the 33rd International Conference on Machine Learning.New York:ACM,2016:1670-1679. [3]BONNER S,VASILE F.Causal embeddings forrecommenda-tion[C]//Proceedings of the 12th ACM Conference on Recommender Systems.New York:ACM,2018:104-112. [4]ZHENG Y,GAO C,LI X,et al.Disentangling user interest and conformity forrecommendation with causal embedding[C]//Proceedings of the Web Conference 2021.New York:ACM,2021:2980-2991. [5]ZHANG Y,FENG F L,HE X N,et al.Causalintervention forleveraging popularity bias in recommendation[C]//Proceedings of the 44th ACM SIGIR Conference on Research and Development in Information Retrieval,2021.New York:ACM,2021:11-20. [6]PEARL J.Causality(2nd ed)[M].New York:Cambridge univer-sity press,2009:65-106. [7]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:bayesian personalized ranking from implicit feedback[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence,2009.AUAI,2009:452-461. [8]XU S Y,TAN J T,HEINECKE S,et al.Deconfounded causal collaborative filtering[J].arXiv:2110.07122,2021. [9]WEI T X,FENG F L,CHEN J W,et al.Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,Virtual Event,2021.New York:ACM,2021:1791-1800. [10]WANG W J,FENG F L,HE X N,et al.Deconfounded recommendation for alleviating bias amplification[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,2021.New York:ACM,2021:1717-1725. [11]GLYMOUR M,PEARL J,JEWELL N.PCausal inference instatistics:a primer[M].New York:John Wiley & Sons,2016. [12]KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].the IEEE Computer Society,2009,42(8):30-37. [13]CLEVERT D A,UNTERTHINER T,HOCHREITER S.Fastand accurate deep network learning by exponential linear units(elus) [C]//Proceedings of the 4th International Conference on Learning Representations,2016.OpenReview.net,2016. [14]SONG W P,XIAO Z P,WANG Y F,et al.Session-Based social recommendation via dynamic graph attention networks[C]//Proceedings of the 12th ACM International Conference on Web Search and Data Mining,2019.New York:ACM,2019:555-563. [15]KINGMA D P,BA J.Adam:a method for stochastic optimization[J].arXiv:1412.6980,2014. |
[1] | JIN Tiancheng, DOU Liang, ZHANG Wei, XIAO Chunyun, LIU Feng, ZHOU Aimin. OJ Exercise Recommendation Model Based on Deep Reinforcement Learning and Program Analysis [J]. Computer Science, 2023, 50(8): 58-67. |
[2] | ZHANG Tao, CHENG Yifei, SUN Xinxu. Graph Attention Networks Based on Causal Inference [J]. Computer Science, 2023, 50(6A): 220600230-9. |
[3] | YANG Bin, LIANG Jing, ZHOU Jiawei, ZHAO Mengci. Study on Interpretable Click-Through Rate Prediction Based on Attention Mechanism [J]. Computer Science, 2023, 50(5): 12-20. |
[4] | LIU Zejing, WU Nan, HUANG Fuqun, SONG You. Hybrid Programming Task Recommendation Model Based on Knowledge Graph and Collaborative Filtering for Online Judge [J]. Computer Science, 2023, 50(2): 106-114. |
[5] | ZHANG Qi, YU Shuangyuan, YIN Hongfeng, XU Baomin. Neural Collaborative Filtering for Social Recommendation Algorithm Based on Graph Attention [J]. Computer Science, 2023, 50(2): 115-122. |
[6] | HAO Jingyu, WEN Jingxuan, LIU Huafeng, JING Liping, YU Jian. Deep Disentangled Collaborative Filtering with Graph Global Information [J]. Computer Science, 2023, 50(1): 41-51. |
[7] | CHENG Zhang-tao, ZHONG Ting, ZHANG Sheng-ming, ZHOU Fan. Survey of Recommender Systems Based on Graph Learning [J]. Computer Science, 2022, 49(9): 1-13. |
[8] | WANG Guan-yu, ZHONG Ting, FENG Yu, ZHOU Fan. Collaborative Filtering Recommendation Method Based on Vector Quantization Coding [J]. Computer Science, 2022, 49(9): 48-54. |
[9] | SUN Xiao-han, ZHANG Li. Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace [J]. Computer Science, 2022, 49(7): 50-56. |
[10] | CAI Xiao-juan, TAN Wen-an. Improved Collaborative Filtering Algorithm Combining Similarity and Trust [J]. Computer Science, 2022, 49(6A): 238-241. |
[11] | HE Yi-chen, MAO Yi-jun, XIE Xian-fen, GU Wan-rong. Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation [J]. Computer Science, 2022, 49(6A): 272-279. |
[12] | GUO Liang, YANG Xing-yao, YU Jiong, HAN Chen, HUANG Zhong-hao. Hybrid Recommender System Based on Attention Mechanisms and Gating Network [J]. Computer Science, 2022, 49(6): 158-164. |
[13] | CHEN Zhuang, ZOU Hai-tao, ZHENG Shang, YU Hua-long, GAO Shang. Diversity Recommendation Algorithm Based on User Coverage and Rating Differences [J]. Computer Science, 2022, 49(5): 159-164. |
[14] | WU Mei-lin, HUANG Jia-jin, QIN Jin. Disentangled Sequential Variational Autoencoder for Collaborative Filtering [J]. Computer Science, 2022, 49(12): 163-169. |
[15] | YUAN De-sen, LIU Xiu-jing, WU Qing-bo, LI Hong-liang, MENG Fan-man, NGAN King-ngi, XU Lin-feng. Visual Question Answering Method Based on Counterfactual Thinking [J]. Computer Science, 2022, 49(12): 229-235. |
|