Defu Lian
Enhong Chen
ABSTRACT
A dozen recommendation libraries have recently been developed to accommodate popular recommendation algorithms for reproducibility. However, they are almost simply a collection of algorithms, overlooking the modularization of recommendation algorithms and their usage in practical scenarios. Algorithmic modularization has the following advantages: 1) helps to understand the effectiveness of each algorithm; 2) easily assembles new algorithms with well-performed modules by either drag-and-drop programming or automatic machine learning; 3) enables reinforcement between algorithms since one algorithm may act as a module of another algorithm. To this end, we develop a highly-modularized recommender system -- RecStudio, in which any recommendation algorithm is categorized into either a ranker or a retriever. In the RecStudio library, we implement 90 recommendation algorithms with the pure Pytorch, covering both common algorithms in other libraries and complex algorithms involving multiple recommendation models. RecStudio is featured from several perspectives, such as index-supported efficient recommendation and evaluation, GPU-accelerated negative sampling, hyperparameter learning on the validation, and cooperation between the retriever and ranker. RecStudio is also equipped with a web service, where the recommendation pipeline can be quickly established and visually evaluated on selected datasets, and the evaluation results are automatically archived and visualized in a leaderboard. The project and documents are released at http://recstudio.org.cn.
Supplemental Material
- Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: a system for large-scale machine learning.. In Osdi, Vol. 16. Savannah, GA, USA, 265--283.Google Scholar
Digital Library
- Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018. Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms, Vol. 11, 9 (2018), 137.Google ScholarCross Ref
- Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, and Tommaso Di Noia. 2021. Elliot: a comprehensive and rigorous framework for reproducible recommender systems evaluation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 2405--2414.Google ScholarDigital Library
- James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. Journal of machine learning research, Vol. 13, 2 (2012).Google Scholar
- James Bergstra, Daniel Yamins, and David Cox. 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International conference on machine learning. PMLR, 115--123.Google Scholar
- Xiangnan He Xiang Wang Bin Wu, Zhongchuan Sun and Jonathan Staniforth. 2019. NeuRec. https://github.com/wubinzzu/NeuRecGoogle Scholar
- Guy Blanc and Steffen Rendle. 2018. Adaptive sampled softmax with kernel based sampling. In International Conference on Machine Learning. PMLR, 590--599.Google Scholar
- Stephen Bonner and Flavian Vasile. 2018. Causal embeddings for recommendation. In Proceedings of the 12th ACM conference on recommender systems. 104--112.Google ScholarDigital Library
- Marcel Caraciolo, Bruno Melo, and Ricardo Caspirro. 2011. Crab: A recommendation engine framework for python. Jarrodmillman Com (2011).Google Scholar
- Pablo Castells, Neil Hurley, and Saul Vargas. 2021. Novelty and diversity in recommender systems. In Recommender systems handbook. Springer, 603--646.Google Scholar
- Beidi Chen, Zichang Liu, Binghui Peng, Zhaozhuo Xu, Jonathan Lingjie Li, Tri Dao, Zhao Song, Anshumali Shrivastava, and Christopher Re. 2021. Mongoose: A learnable lsh framework for efficient neural network training. In International Conference on Learning Representations.Google Scholar
- Chong Chen, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. 2020. Efficient neural matrix factorization without sampling for recommendation. ACM Transactions on Information Systems (TOIS), Vol. 38, 2 (2020), 1--28.Google ScholarDigital Library
- Jin Chen, Defu Lian, Binbin Jin, Xu Huang, Kai Zheng, and Enhong Chen. 2022a. Fast variational autoencoder with inverted multi-index for collaborative filtering. In Proceedings of the ACM Web Conference 2022. 1944--1954.Google ScholarDigital Library
- Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley, and Caiming Xiong. 2022b. Intent contrastive learning for sequential recommendation. In Proceedings of the ACM Web Conference 2022. 2172--2182.Google ScholarDigital Library
- Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. 7--10.Google ScholarDigital Library
- Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. 191--198.Google ScholarDigital Library
- Arthur da Costa, Eduardo Fressato, Fernando Neto, Marcelo Manzato, and Ricardo Campello. 2018. Case recommender: a flexible and extensible python framework for recommender systems. In Proceedings of the 12th ACM Conference on Recommender Systems. 494--495.Google ScholarDigital Library
- Stefan Falkner, Aaron Klein, and Frank Hutter. 2018. BOHB: Robust and efficient hyperparameter optimization at scale. In International Conference on Machine Learning. PMLR, 1437--1446.Google Scholar
- Chao Feng, Wuchao Li, Defu Lian, Zheng Liu, and Enhong Chen. 2022a. Recommender Forest for Efficient Retrieval. Advances in Neural Information Processing Systems, Vol. 35 (2022), 8912--38924.Google Scholar
- Chao Feng, Defu Lian, Zheng Liu, Xing Xie, Le Wu, and Enhong Chen. 2022b. Forest-based Deep Recommender. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 523--532.Google ScholarDigital Library
- Luke Gallagher, Ruey-Cheng Chen, Roi Blanco, and J Shane Culpepper. 2019. Joint optimization of cascade ranking models. In Proceedings of the twelfth ACM international conference on web search and data mining. 15--23.Google ScholarDigital Library
- Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2011. MyMediaLite: A Free Recommender System Library. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys 2011).Google ScholarDigital Library
- Guibing Guo, Jie Zhang, Zhu Sun, and Neil Yorke-Smith. 2015. Librec: A java library for recommender systems.. In UMAP workshops, Vol. 4. Citeseer, 38--45.Google Scholar
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. (2017), 1725--1731.Google Scholar
- Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar. 2020. Accelerating Large-Scale Inference with Anisotropic Vector Quantization. In International Conference on Machine Learning.Google Scholar
- Gaurav Gupta, Tharun Medini, Anshumali Shrivastava, and Alexander J Smola. 2022. BLISS: A Billion scale Index using Iterative Re-partitioning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 486--495.Google ScholarDigital Library
- Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017a. Translation-based recommendation. In Proceedings of the eleventh ACM conference on recommender systems. 161--169.Google ScholarDigital Library
- Xiangnan He and Tat-Seng Chua. 2017. Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. 355--364.Google ScholarDigital Library
- Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 639--648.Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017b. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173--182.Google ScholarDigital Library
- Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, and Deborah Estrin. 2017. Collaborative metric learning. In Proceedings of the 26th international conference on world wide web. 193--201.Google ScholarDigital Library
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining. Ieee, 263--272.Google ScholarDigital Library
- Xu Huang, Defu Lian, Jin Chen, Zheng Liu, Xing Xie, and Enhong Chen. 2022. Cooperative Retriever and Ranker in Deep Recommenders. arXiv preprint arXiv:2206.14649 (2022).Google Scholar
- Nicolas Hug. 2020. Surprise: A Python library for recommender systems. Journal of Open Source Software, Vol. 5, 52 (2020), 2174. https://doi.org/10.21105/joss.02174Google ScholarCross Ref
- Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. 2011. Sequential model-based optimization for general algorithm configuration. In Learning and Intelligent Optimization: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011. Selected Papers 5. Springer, 507--523.Google ScholarDigital Library
- Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, et al. 2017. Population based training of neural networks. arXiv preprint arXiv:1711.09846 (2017).Google Scholar
- Dietmar Jannach, Pearl Pu, Francesco Ricci, and Markus Zanker. 2022. Recommender systems: Trends and frontiers., 145--150 pages.Google Scholar
- Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, Vol. 7, 3 (2019), 535--547.Google ScholarCross Ref
- Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). IEEE, 197--206.Google ScholarCross Ref
- Maciej Kula. 2015. Metadata Embeddings for User and Item Cold-start Recommendations. In Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender Systems co-located with 9th ACM Conference on Recommender Systems (RecSys 2015), Vienna, Austria, September 16-20, 2015., Toine Bogers and Marijn Koolen (Eds.), Vol. 1448. 14--21.Google Scholar
- Maciej Kula. 2017. Spotlight. https://github.com/maciejkula/spotlight.Google Scholar
- Hugo Larochelle, Dumitru Erhan, Aaron Courville, James Bergstra, and Yoshua Bengio. 2007. An empirical evaluation of deep architectures on problems with many factors of variation. In Proceedings of the 24th international conference on Machine learning. 473--480.Google ScholarDigital Library
- Yann LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller. 2002. Efficient backprop. In Neural networks: Tricks of the trade. Springer, 9-50.Google ScholarDigital Library
- Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. 2017. Hyperband: A novel bandit-based approach to hyperparameter optimization. The Journal of Machine Learning Research, Vol. 18, 1 (2017), 6765--6816.Google ScholarDigital Library
- Zhao Lucis Li, Chieh-Jan Mike Liang, Wenjia He, Lianjie Zhu, Wenjun Dai, Jin Jiang, and Guangzhong Sun. 2018. Metis: Robustly tuning tail latencies of cloud systems. In 2018 {USENIX} Annual Technical Conference ({USENIX}{ATC} 18). 981--992.Google ScholarDigital Library
- Defu Lian, Qi Liu, and Enhong Chen. 2020. Personalized ranking with importance sampling. In Proceedings of The Web Conference 2020. 1093--1103.Google ScholarDigital Library
- Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018. xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1754--1763.Google ScholarDigital Library
- Dawen Liang, Laurent Charlin, and David M Blei. 2016a. Causal inference for recommendation. In Causation: Foundation to Application, Workshop at UAI. AUAI.Google Scholar
- Dawen Liang, Laurent Charlin, James McInerney, and David M Blei. 2016b. Modeling user exposure in recommendation. In Proceedings of the 25th international conference on World Wide Web. 951--961.Google ScholarDigital Library
- Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 2018 world wide web conference. 689--698.Google ScholarDigital Library
- Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica. 2018. Tune: A Research Platform for Distributed Model Selection and Training. arXiv preprint arXiv:1807.05118 (2018).Google Scholar
- Zihan Lin, Changxin Tian, Yupeng Hou, and Wayne Xin Zhao. 2022. Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In Proceedings of the ACM Web Conference 2022. 2320--2329.Google ScholarDigital Library
- Zhiwei Liu, Yongjun Chen, Jia Li, Philip S Yu, Julian McAuley, and Caiming Xiong. 2021. Contrastive self-supervised sequential recommendation with robust augmentation. arXiv preprint arXiv:2108.06479 (2021).Google Scholar
- Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, and Xiuqiang He. 2021. SimpleX: A simple and strong baseline for collaborative filtering. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1243--1252.Google ScholarDigital Library
- Zaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis, Siwei Liu, Yaxiong Wu, Xi Wang, Shangsong Liang, Yucheng Liang, Guangtao Zeng, et al. 2020. BETA-Rec: Build, Evaluate and Tune Automated Recommender Systems. In Fourteenth ACM Conference on Recommender Systems. 588--590.Google Scholar
- Microsoft. 2021. Neural Network Intelligence. https://github.com/microsoft/nniGoogle Scholar
- Jiarui Qin, Jiachen Zhu, Bo Chen, Zhirong Liu, Weiwen Liu, Ruiming Tang, Rui Zhang, Yong Yu, and Weinan Zhang. 2022. RankFlow: Joint Optimization of Multi-Stage Cascade Ranking Systems as Flows. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 814--824.Google ScholarDigital Library
- Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V Le, and Alexey Kurakin. 2017. Large-scale evolution of image classifiers. In International Conference on Machine Learning. PMLR, 2902--2911.Google Scholar
- Steffen Rendle. 2010. Factorization machines. In 2010 IEEE International conference on data mining. IEEE, 995--1000.Google ScholarDigital Library
- Steffen Rendle. 2012. Factorization Machines with libFM. ACM Trans. Intell. Syst. Technol., Vol. 3, 3, Article 57 (May 2012), 22 pages.Google ScholarDigital Library
- Steffen Rendle and Christoph Freudenthaler. 2014. Improving pairwise learning for item recommendation from implicit feedback. In Proceedings of the 7th ACM international conference on Web search and data mining. 273--282.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. (2009), 452--461.Google ScholarDigital Library
- Yuta Saito. 2020. Unbiased pairwise learning from biased implicit feedback. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5--12.Google ScholarDigital Library
- Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020. Unbiased recommender learning from missing-not-at-random implicit feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining. 501--509.Google ScholarDigital Library
- Aghiles Salah, Quoc-Tuan Truong, and Hady W Lauw. 2020. Cornac: A Comparative Framework for Multimodal Recommender Systems. Journal of Machine Learning Research, Vol. 21, 95 (2020), 1--5.Google Scholar
- Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In international conference on machine learning. PMLR, 1670--1679.Google Scholar
- Weichen Shen. 2017. DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. https://github.com/shenweichen/deepctr.Google Scholar
- Ryan Spring and Anshumali Shrivastava. 2017. A new unbiased and efficient class of lsh-based samplers and estimators for partition function computation in log-linear models. arXiv preprint arXiv:1703.05160 (2017).Google Scholar
- Zhu Sun, Hui Fang, Jie Yang, Xinghua Qu, Hongyang Liu, Di Yu, Yew-Soon Ong, and Jie Zhang. 2022. DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022).Google Scholar
- Scikit-Optimize Team. 2016. Scikit-Optimize. https://scikit-optimize.github.io/stable/Google Scholar
- Chenyang Wang, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma. 2020. Make it a chorus: knowledge-and time-aware item modeling for sequential recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 109--118.Google ScholarDigital Library
- Lidan Wang, Jimmy Lin, and Donald Metzler. 2011. A cascade ranking model for efficient ranked retrieval. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 105--114.Google ScholarDigital Library
- Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. In Proceedings of the ADKDD'17. 1--7.Google ScholarDigital Library
- Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, and Xiangnan He. 2021. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1791--1800.Google ScholarDigital Library
- Jason Weston, Samy Bengio, and Nicolas Usunier. 2011. Wsabie: Scaling up to large vocabulary image annotation. (2011), 2764--2770.Google Scholar
- Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 726--735.Google ScholarDigital Library
- Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022. Contrastive learning for sequential recommendation. In 2022 IEEE 38th international conference on data engineering (ICDE). IEEE, 1259--1273.Google ScholarCross Ref
- Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed Chi. 2019. Sampling-bias-corrected neural modeling for large corpus item recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems. 269--277.Google ScholarDigital Library
- Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. 2022. Are graph augmentations necessary? simple graph contrastive learning for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1294--1303.Google Scholar
- Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016a. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 353--362.Google ScholarDigital Library
- Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016b. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 353--362.Google ScholarDigital Library
- Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu. 2013. Optimizing top-n collaborative filtering via dynamic negative item sampling. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 785--788.Google ScholarDigital Library
- Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021. Causal intervention for leveraging popularity bias in recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 11--20.Google ScholarDigital Library
- Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, et al. 2022. RecBole 2.0: Towards a More Up-to-Date Recommendation Library. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 4722--4726.Google ScholarDigital Library
- Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, et al. 2021. Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 4653--4664.Google ScholarDigital Library
- Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, and Depeng Jin. 2021. Disentangling user interest and conformity for recommendation with causal embedding. In Proceedings of the Web Conference 2021. 2980--2991.Google ScholarDigital Library
- Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1059--1068.Google ScholarDigital Library
- Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, and Kun Gai. 2018. Learning tree-based deep model for recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1079--1088.Google ScholarDigital Library
- Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, and Xiuqiang He. 2021. Open benchmarking for click-through rate prediction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2759--2769.Google ScholarDigital Library
- Barret Zoph and Quoc Le. 2017. Neural Architecture Search with Reinforcement Learning. (2017).Google Scholar
Index Terms
- RecStudio: Towards a Highly-Modularized Recommender System
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