ACM Conference on Recommender Systems - Google Scholar Metrics

h5-index:46
h5-median:73

h5-index is the h-index for articles published in the last 5 complete years. It is the largest number h such that h articles published in 2016-2020 have at least h citations each.hide

h5-median for a publication is the median number of citations for the articles that make up its h5-index.hide

Title / Author	Cited by	Year
Deep Neural Networks for YouTube Recommendations P Covington, J Adams, E Sargin Proceedings of the 10th ACM Conference on Recommender Systems, 191-198	927	2016
Convolutional Matrix Factorization for Document Context-Aware Recommendation D Kim, C Park, J Oh, S Lee, H Yu Proceedings of the 10th ACM Conference on Recommender Systems, 233-240	317	2016
Field-aware Factorization Machines for CTR Prediction Y Juan, Y Zhuang, WS Chin, CJ Lin Proceedings of the 10th ACM Conference on Recommender Systems, 43-50	242	2016
Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations B Hidasi, M Quadrana, A Karatzoglou, D Tikk Proceedings of the 10th ACM Conference on Recommender Systems, 241-248	182	2016
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	177	2017
Context-Aware Event Recommendation in Event-based Social Networks AQ Macedo, LB Marinho, RLT Santos Proceedings of the 9th ACM Conference on Recommender Systems, 123-130	163	2015
Ask the GRU: Multi-task Learning for Deep Text Recommendations T Bansal, D Belanger, A McCallum Proceedings of the 10th ACM Conference on Recommender Systems, 107-114	163	2016
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence D Liang, J Altosaar, L Charlin, DM Blei Proceedings of the 10th ACM Conference on Recommender Systems, 59-66	155	2016
A Probabilistic Model for Using Social Networks in Personalized Item Recommendation AJB Chaney, DM Blei, T Eliassi-Rad Proceedings of the 9th ACM Conference on Recommender Systems, 43-50	148	2015
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	142	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	140	2017
Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation F Vasile, E Smirnova, A Conneau Proceedings of the 10th ACM Conference on Recommender Systems, 225-232	130	2016
Translation-based Recommendation R He, WC Kang, J McAuley Proceedings of the Eleventh ACM Conference on Recommender Systems, 161-169	130	2017
TransNets: Learning to Transform for Recommendation R Catherine, W Cohen Proceedings of the Eleventh ACM Conference on Recommender Systems, 288-296	107	2017
Fast Differentially Private Matrix Factorization Z Liu, YX Wang, A Smola Proceedings of the 9th ACM Conference on Recommender Systems, 171-178	92	2015
Learning Distributed Representations from Reviews for Collaborative Filtering A Almahairi, K Kastner, K Cho, A Courville Proceedings of the 9th ACM Conference on Recommender Systems, 147-154	90	2015
Sequential User-based Recurrent Neural Network Recommendations T Donkers, B Loepp, J Ziegler Proceedings of the Eleventh ACM Conference on Recommender Systems, 152-160	88	2017
Recommendations with a Purpose D Jannach, G Adomavicius Proceedings of the 10th ACM Conference on Recommender Systems, 7-10	84	2016
Dynamic Poisson Factorization L Charlin, R Ranganath, J McInerney, DM Blei Proceedings of the 9th ACM Conference on Recommender Systems, 155-162	82	2015
HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems P Kouki, S Fakhraei, J Foulds, M Eirinaki, L Getoor Proceedings of the 9th ACM Conference on Recommender Systems, 99-106	81	2015

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Dates and citation counts are estimated and are determined automatically by a computer program.