ACM Conference on Recommender Systems - Google Scholar Metrics

h5-index:45
h5-median:65

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 2014-2018 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	494	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	184	2016
Ratings meet reviews, a combined approach to recommend G Ling, MR Lyu, I King Proceedings of the 8th ACM Conference on Recommender systems, 105-112	171	2014
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	133	2016
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	128	2015
Beyond clicks: dwell time for personalization X Yi, L Hong, E Zhong, NN Liu, S Rajan Proceedings of the 8th ACM Conference on Recommender systems, 113-120	120	2014
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	109	2016
Comparative recommender system evaluation: benchmarking recommendation frameworks A Said, A Bellogín Proceedings of the 8th ACM Conference on Recommender systems, 129-136	108	2014
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	105	2015
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	100	2016
Item cold-start recommendations: learning local collective embeddings M Saveski, A Mantrach Proceedings of the 8th ACM Conference on Recommender systems, 89-96	100	2014
User perception of differences in recommender algorithms MD Ekstrand, FM Harper, MC Willemsen, JA Konstan Proceedings of the 8th ACM Conference on Recommender systems, 161-168	99	2014
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	96	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	95	2017
Inferring user interests in the Twitter social network P Bhattacharya, MB Zafar, N Ganguly, S Ghosh, KP Gummadi Proceedings of the 8th ACM Conference on Recommender systems, 357-360	95	2014
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	83	2016
Offline and online evaluation of news recommender systems at swissinfo. ch F Garcin, B Faltings, O Donatsch, A Alazzawi, C Bruttin, A Huber Proceedings of the 8th ACM Conference on Recommender systems, 169-176	80	2014
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	68	2017
CSLIM: contextual SLIM recommendation algorithms Y Zheng, B Mobasher, R Burke Proceedings of the 8th ACM Conference on Recommender systems, 301-304	67	2014
Social collaborative filtering for cold-start recommendations S Sedhain, S Sanner, D Braziunas, L Xie, J Christensen Proceedings of the 8th ACM Conference on Recommender systems, 345-348	66	2014

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