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h5 指数:47
h5 中位数:111

h5 指数是指在过去整整 5 年中所发表文章的 h 指数。h 指在 2018-2022 年间发表的 h 篇文章每篇至少都被引用过 h 次的最大值。隐藏

出版物的 h5 中位数，是指出版物的 h5 指数所涵盖的所有文章获得的引用次数的中位值。隐藏

标题/作者	引用次数	年份
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks M Quadrana, A Karatzoglou, B Hidasi, P Cremonesi Proceedings of the Eleventh ACM Conference on Recommender Systems, 130-137	434	2017
Are we really making much progress? A worrying analysis of recent neural recommendation approaches MF Dacrema, P Cremonesi, D Jannach Proceedings of the 13th ACM Conference on Recommender Systems, 101-109	394	2019
Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction S Seo, J Huang, H Yang, Y Liu Proceedings of the Eleventh ACM Conference on Recommender Systems, 297-305	352	2017
When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation D Jannach, M Ludewig Proceedings of the Eleventh ACM Conference on Recommender Systems, 306-310	300	2017
Deep reinforcement learning for page-wise recommendations X Zhao, L Xia, L Zhang, Z Ding, D Yin, J Tang Proceedings of the 12th ACM Conference on Recommender Systems, 95-103	281	2018
Translation-based Recommendation R He, WC Kang, J McAuley Proceedings of the Eleventh ACM Conference on Recommender Systems, 161-169	277	2017
Sequential User-based Recurrent Neural Network Recommendations T Donkers, B Loepp, J Ziegler Proceedings of the Eleventh ACM Conference on Recommender Systems, 152-160	228	2017
TransNets: Learning to Transform for Recommendation R Catherine, W Cohen Proceedings of the Eleventh ACM Conference on Recommender Systems, 288-296	218	2017
Controlling Popularity Bias in Learning-to-Rank Recommendation H Abdollahpouri, R Burke, B Mobasher Proceedings of the Eleventh ACM Conference on Recommender Systems, 42-46	208	2017
Recurrent knowledge graph embedding for effective recommendation Z Sun, J Yang, J Zhang, A Bozzon, LK Huang, C Xu Proceedings of the 12th ACM Conference on Recommender Systems, 297-305	191	2018
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility AJB Chaney, BM Stewart, BE Engelhardt Proceedings of the 12th ACM Conference on Recommender Systems, 224-232	174	2018
Recommending what video to watch next: a multitask ranking system Z Zhao, L Hong, L Wei, J Chen, A Nath, S Andrews, A Kumthekar, ... Proceedings of the 13th ACM Conference on Recommender Systems, 43-51	159	2019
Neural Collaborative Filtering vs. Matrix Factorization Revisited S Rendle, W Krichene, L Zhang, J Anderson Fourteenth ACM Conference on Recommender Systems, 240-248	148	2020
Spectral collaborative filtering L Zheng, CT Lu, F Jiang, J Zhang, PS Yu Proceedings of the 12th ACM Conference on Recommender Systems, 311-319	129	2018
entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation E Palumbo, G Rizzo, R Troncy Proceedings of the Eleventh ACM Conference on Recommender Systems, 32-36	126	2017
Item recommendation on monotonic behavior chains M Wan, J McAuley Proceedings of the 12th ACM Conference on Recommender Systems, 86-94	125	2018
Calibrated recommendations H Steck Proceedings of the 12th ACM Conference on Recommender Systems, 154-162	124	2018
3D Convolutional Networks for Session-based Recommendation with Content Features TX Tuan, TM Phuong Proceedings of the Eleventh ACM Conference on Recommender Systems, 138-146	123	2017
Causal embeddings for recommendation S Bonner, F Vasile Proceedings of the 12th ACM Conference on Recommender Systems, 104-112	122	2018
Word2vec applied to recommendation: hyperparameters matter H Caselles-Dupré, F Lesaint, J Royo-Letelier Proceedings of the 12th ACM Conference on Recommender Systems, 352-356	121	2018

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