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The evaluation of recommender systems from a practical perspective is a topic of ongoing discourse within the research community.
Triple2Vec: Learning Triple Embeddings from Knowledge Graphs
Valeria Fionda and Giuseppe Pirrò. 2019 · 1905
Earlier work this paper cites.
Item-Based Collaborative Filtering Recommendation Algorithms. In Proceedings of the 10th International Conference on World Wide Web (Hong Kong, Hong Kong) (WWW ’01) . Association for Computing Machinery, New York, NY, USA, 285–295
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
Earlier work this paper cites.
Time weight collaborative filtering
Yi Ding and Xue Li. 2005 · 2005
Earlier work this paper cites.
The netflix prize. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA, August 12-15, 2007 . ACM
James Bennett and Stan Lanning. 2007 · 2007
Earlier work this paper cites.
Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario
Federico Bianchi, Jacopo Tagliabue, Bingqing Yu, Luca Bigon, and Ciro Greco. 2020 · 2007
Earlier work this paper cites.
Collaborative Filtering for Implicit Feedback Datasets. In 2008 Eighth IEEE International Conference on Data Mining . 263–272
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008 · 2008
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback. In UAI . 452–461
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Group recommendations with rank aggregation and collaborative filtering. In RecSys . 119–126
Linas Baltrunas, Tadas Makcinskas, and Francesco Ricci. 2010 · 2010
Earlier work this paper cites.
Performance of recommender algorithms on top-N recommendation tasks. In RecSys
Paolo Cremonesi, Yehuda Koren, and Turrin Roberto. 2010 · 2010
Earlier work this paper cites.
On Offline Evaluation of Recommender Systems
Yitong Ji, Aixin Sun, Jie Zhang, and Chenliang Li. 2020 · 2010
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A Critical Study on Data Leakage in Recommender System Offline Evaluation
Yitong Ji, Aixin Sun, Jie Zhang, and Chenliang Li. 2022 · 2010
Earlier work this paper cites.
Factorizing personalized markov chains for next-basket recommendation. In WWW . 811–820
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010 · 2010
Earlier work this paper cites.
Towards a more realistic evaluation: testing the ability to predict future tastes of matrix factorization-based recommenders. In RecSys . 309–312
Pedro G Campos, Fernando Díez, and Manuel Sánchez-Montañés. 2011 · 2011
Earlier work this paper cites.
Slim: Sparse linear methods for top-n recommender systems. In ICDM
Xia Ning and George Karypis. 2011 · 2011
Earlier work this paper cites.
AutoRec: Autoencoders Meet Collaborative Filtering. In WWW . 111–112
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015 · 2015
Earlier work this paper cites.
VBPR: visual Bayesian Personalized Ranking from implicit feedback. In AAAI . 144–150
Ruining He and Julian McAuley. 2016 · 2016
Earlier work this paper cites.
Collaborative denoising auto-encoders for top-n recommender systems. In WSDM . 153–162
Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016 · 2016
Earlier work this paper cites.
It’s Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal]
Joeran Beel. 2017 · 2017
Cited alongside, same era.
Neural collaborative filtering. In WWW . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Collaborative Metric Learning. In WWW . 193–201
Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, and Deborah Estrin. 2017 · 2017
Cited alongside, same era.
Collaborative Variational Autoencoder for Recommender Systems. In SIGKDD . 305–314
Xiaopeng Li and James She. 2017 · 2017
Cited alongside, same era.
Fairness-aware group recommendation with pareto-efficiency. In RecSys . 107–115
Lin Xiao, Zhang Min, Zhang Yongfeng, Gu Zhaoquan, Liu Yiqun, and Ma Shaoping. 2017 · 2017
Cited alongside, same era.
Sequential Variational Autoencoders for Collaborative Filtering. In WSDM . 600–608
Noveen Sachdeva, Giuseppe Manco, Ettore Ritacco, and Vikram Pudi. 2019 · 2019
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BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In CIKM . 1441–1450
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
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Dynamic Collaborative Recurrent Learning. In CIKM . 1151–1160
Teng Xiao, Shangsong Liang, and Zaiqiao Meng. 2019 · 2019
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Multi-order attentive ranking model for sequential recommendation. In AAAI . 5709–5716
Lu Yu, Chuxu Zhang, Shangsong Liang, and Xiangliang Zhang. 2019 · 2019
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Offline evaluation options for recommender systems
Rocío Cañamares, Pablo Castells, and Alistair Moffat. 2020 · 2020
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Recsys Challenge 2018: Automatic Music Playlist Continuation. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18) . Association for Computing Machinery, New York, NY, USA, 527–528
Ching-Wei Chen, Paul Lamere, Markus Schedl, and Hamed Zamani. 2018 · 2018
Cited alongside, same era.
Self-attentive sequential recommendation. In ICDM . 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Cited alongside, same era.
Variational autoencoders for collaborative filtering. In WWW . 689–698
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018 · 2018
Cited alongside, same era.
Coevolutionary Recommendation Model: Mutual Learning between Ratings and Reviews. In WWW . 773–782
Yichao Lu, Ruihai Dong, and Barry Smyth. 2018 · 2018
Cited alongside, same era.
Representing and Recommending Shopping Baskets with Complementarity, Compatibility and Loyalty. In CIKM . 1133–1142
Mengting Wan, Di Wang, Jie Liu, Paul Bennett, and Julian McAuley. 2018 · 2018
Cited alongside, same era.
CTRec: A Long-Short Demands Evolution Model for Continuous-Time Recommendation. In SIGIR . 675–684
Ting Bai, Lixin Zou, Wayne Xin Zhao, Pan Du, Weidong Liu, Jian-Yun Nie, and Ji-Rong Wen. 2019 · 2019
Cited alongside, same era.
Top-K Off-Policy Correction for a REINFORCE Recommender System. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (Melbourne VIC, Australia) (WSDM ’19) . Association for Computing Machinery, New York, NY, USA, 456–464
Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, and Ed H. Chi. 2019 · 2019
Cited alongside, same era.
Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance. In RecSys . 101–110
Mesut Kaya, Derek Bridge, and Nava Tintarev. 2020 · 2020
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A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks
Hea In Lee, Il Young Choi, Hyun Sil Moon, and Jae Kyeong Kim. 2020 · 2020
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Time Interval Aware Self-Attention for Sequential Recommendation. In WSDM . 322–330
Jiacheng Li, Yujie Wang, and Julian McAuley. 2020 · 2020
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Exploring Data Splitting Strategies for the Evaluation of Recommendation Models. In Fourteenth ACM Conference on Recommender Systems (Virtual Event, Brazil) (RecSys ’20) . Association for Computing Machinery, New York, NY, USA, 681–686
Zaiqiao Meng, Richard McCreadie, Craig Macdonald, and Iadh Ounis. 2020 · 2020
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Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison. In Proceedings of the 14th ACM Conference on Recommender Systems
Zhu Sun, Di Yu, Hui Fang, Jie Yang, Xinghua Qu, Jie Zhang, and Cong Geng. 2020 · 2020
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Learning a Joint Search and Recommendation Model from User-Item Interactions. In WSDM . 717–725
Hamed Zamani and W Bruce Croft. 2020 · 2020
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Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation. In RecSys . 43–52
Yin Zhang, Ziwei Zhu, Yun He, and James Caverlee. 2020 · 2020
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TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations. In RecSys . 338–347
Jin Peng Zhou, Zhaoyue Cheng, Felipe Perez, and Maksims Volkovs. 2020 · 2020
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TTRS: Tinkoff Transactions Recommender System benchmark
Sergey Kolesnikov, Oleg Lashinin, Michail Pechatov, and Alexander Kosov. 2021 · 2021
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Time-dependent evaluation of recommender systems. In Perspectives 2021 (CEUR Workshop Proceedings, Vol. 2955) . CEUR Workshop Proceedings
Teresa Scheidt and Joeran Beel. 2021 · 2021
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Challenges and Research Opportunities in ECommerce Search and Recommendations
Manos Tsagkias, Tracy Holloway King, Surya Kallumadi, Vanessa Murdock, and Maarten de Rijke. 2021 · 2021
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Exploring lottery ticket hypothesis in media recommender systems
Yanfang Wang, Yongduo Sui, Xiang Wang, Zhenguang Liu, and Xiangnan He. 2021 · 2021
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