ABSTRACT
Recommender systems developers are constantly faced with difficult design decisions. Additionally, the number of options that a recommender systems developer has to consider continually grows over time with new innovations. The machine learning community is in a similar situation and has come together to tackle the problem. They invented concepts and tools to make machine learning development both easier and faster. These developments are categorized as automated machine learning (AutoML). As a result, the AutoML community formed and continuously innovates new approaches. Inspired by AutoML, the recommender systems community has recently understood the need for automation and sparsely introduced AutoRecSys. The goal of AutoRecSys is not to replace recommender systems developers but to improve performance through the automation of design decisions. With AutoRecSys, recommender systems engineers do not have to focus on easy but time-consuming tasks and are free to pursue difficult engineering tasks instead. Additionally, AutoRecSys enables easier access to recommender systems for beginners as it reduces the amount of knowledge required to get started with the development of recommender systems. AutoRecSys, like AutoML, is still early in its development and does not yet cover the whole development pipeline. Additionally, it is not yet clear, under which circumstances AutoML approaches can be transferred to recommender systems. Our research intends to close this gap by improving AutoRecSys both with regard to the transfer of AutoML and novel approaches. Furthermore, we focus specifically on the development of novel automation approaches for data processing and training. We note that the realization of AutoRecSys is going to be a community effort. Our part in this effort is to research AutoRecSys fundamentals, build practical tools for the community, raise awareness of the advantages of automation, and catalyze AutoRecSys development.
- Rohan Anand and Joeran Beel. 2020. Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization. In Proceedings of the 14th ACM Conference on Recommender Systems (Virtual Event, Brazil) (RecSys ’20). Association for Computing Machinery, New York, NY, USA, 585–587. https://doi.org/10.1145/3383313.3411467Google ScholarDigital Library
- Joeran Beel, Marcel Genzmehr, Stefan Langer, Andreas Nürnberger, and Bela Gipp. 2013. A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation. In Proceedings of the international workshop on reproducibility and replication in recommender systems evaluation. 7–14.Google ScholarDigital Library
- Robert M Bell, Yehuda Koren, and Chris Volinsky. 2007. The bellkor solution to the netflix prize. KorBell Team’s Report to Netflix (2007).Google Scholar
- Shlomo Berkovsky, Tsvi Kuflik, and Francesco Ricci. 2008. Mediation of user models for enhanced personalization in recommender systems. User Modeling and User-Adapted Interaction 18 (2008), 245–286.Google ScholarDigital Library
- K Blom, AC Serban, HH Hoos, and JMW Visser. 2021. AutoML adoption in ML software. In 8th ICML Workshop on automated machine learning.Google Scholar
- Rocío Cañamares, Marcos Redondo, and Pablo Castells. 2019. Multi-Armed Recommender System Bandit Ensembles. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 432–436. https://doi.org/10.1145/3298689.3346984Google ScholarDigital Library
- Bo Chen, Xiangyu Zhao, Yejing Wang, Wenqi Fan, Huifeng Guo, and Ruiming Tang. 2023. A Comprehensive Survey on Automated Machine Learning for Recommendations. arxiv:2204.01390 [cs.IR]Google Scholar
- Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023. Bias and Debias in Recommender System: A Survey and Future Directions. ACM Trans. Inf. Syst. 41, 3, Article 67 (feb 2023), 39 pages. https://doi.org/10.1145/3564284Google ScholarDigital Library
- Paolo Cremonesi, Antonio Tripodi, and Roberto Turrin. 2011. Cross-Domain Recommender Systems. In 2011 IEEE 11th International Conference on Data Mining Workshops. 496–503. https://doi.org/10.1109/ICDMW.2011.57Google ScholarDigital Library
- Anamaria Crisan and Brittany Fiore-Gartland. 2021. Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 601, 15 pages. https://doi.org/10.1145/3411764.3445775Google ScholarDigital Library
- Tiago Cunha, Carlos Soares, and André C. P. L. F. de Carvalho. 2018. CF4CF: Recommending Collaborative Filtering Algorithms Using Collaborative Filtering. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). Association for Computing Machinery, New York, NY, USA, 357–361. https://doi.org/10.1145/3240323.3240378Google ScholarDigital Library
- Chris Deotte, Bo Liu, Benedikt Schifferer, and Gilberto Titericz. 2021. GPU Accelerated Boosted Trees and Deep Neural Networks for Better Recommender Systems. In Proceedings of the Recommender Systems Challenge 2021 (Amsterdam, Netherlands) (RecSysChallenge ’21). Association for Computing Machinery, New York, NY, USA, 7–14. https://doi.org/10.1145/3487572.3487605Google ScholarDigital Library
- Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, and Konstantinos Tserpes. 2018. Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions. Future Generation Computer Systems 78 (2018), 413–418. https://doi.org/10.1016/j.future.2017.09.015Google ScholarCross Ref
- Radwa Elshawi, Mohamed Maher, and Sherif Sakr. 2019. Automated Machine Learning: State-of-The-Art and Open Challenges. arxiv:1906.02287 [cs.LG]Google Scholar
- Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2019. Neural architecture search: A survey. The Journal of Machine Learning Research 20, 1 (2019), 1997–2017.Google ScholarDigital Library
- Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, and Alexander Smola. 2020. AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. arxiv:2003.06505 [stat.ML]Google Scholar
- Nick Erickson, Xingjian Shi, James Sharpnack, and Alexander Smola. 2022. Multimodal AutoML for Image, Text and Tabular Data. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD ’22). Association for Computing Machinery, New York, NY, USA, 4786–4787. https://doi.org/10.1145/3534678.3542616Google ScholarDigital Library
- Rasool Fakoor, Jonas W Mueller, Nick Erickson, Pratik Chaudhari, and Alexander J Smola. 2020. Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation. In Advances in Neural Information Processing Systems, Vol. 33. Curran Associates, Inc., 8671–8681.Google Scholar
- Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, and Frank Hutter. 2020. Auto-sklearn 2.0: The next generation. arXiv preprint arXiv:2007.04074 24 (2020).Google Scholar
- Saman Forouzandeh, Kamal Berahmand, and Mehrdad Rostami. 2021. Presentation of a recommender system with ensemble learning and graph embedding: a case on MovieLens. Multimedia Tools and Applications 80 (2021), 7805–7832.Google ScholarDigital Library
- David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry. 1992. Using Collaborative Filtering to Weave an Information Tapestry. Commun. ACM 35, 12 (dec 1992), 61–70. https://doi.org/10.1145/138859.138867Google ScholarDigital Library
- Jyotirmoy Gope and Sanjay Kumar Jain. 2017. A survey on solving cold start problem in recommender systems. In 2017 International Conference on Computing, Communication and Automation (ICCCA). 133–138. https://doi.org/10.1109/CCAA.2017.8229786Google ScholarCross Ref
- Srijan Gupta and Joeran Beel. 2020. Auto-CaseRec: Automatically Selecting and Optimizing Recommendation-Systems Algorithms. https://doi.org/10.31219/osf.io/4znmdGoogle Scholar
- Marc Hanussek, Matthias Blohm, and Maximilien Kintz. 2021. Can AutoML Outperform Humans? An Evaluation on Popular OpenML Datasets Using AutoML Benchmark. In 2020 2nd International Conference on Artificial Intelligence, Robotics and Control (Cairo, MN, Egypt) (AIRC’20). Association for Computing Machinery, New York, NY, USA, 29–32. https://doi.org/10.1145/3448326.3448353Google ScholarDigital Library
- Xin He, Kaiyong Zhao, and Xiaowen Chu. 2021. AutoML: A survey of the state-of-the-art. Knowledge-Based Systems 212 (2021), 106622. https://doi.org/10.1016/j.knosys.2020.106622Google ScholarCross Ref
- Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren. 2019. Automated machine learning: methods, systems, challenges. Springer Nature.Google Scholar
- Karl Krauth, Sarah Dean, Alex Zhao, Wenshuo Guo, Mihaela Curmei, Benjamin Recht, and Michael I Jordan. 2020. Do offline metrics predict online performance in recommender systems?arXiv preprint arXiv:2011.07931 (2020).Google Scholar
- Blerina Lika, Kostas Kolomvatsos, and Stathes Hadjiefthymiades. 2014. Facing the cold start problem in recommender systems. Expert Systems with Applications 41, 4, Part 2 (2014), 2065–2073. https://doi.org/10.1016/j.eswa.2013.09.005Google ScholarDigital Library
- Jie Lu, Dianshuang Wu, Mingsong Mao, Wei Wang, and Guangquan Zhang. 2015. Recommender system application developments: A survey. Decision Support Systems 74 (2015), 12–32. https://doi.org/10.1016/j.dss.2015.03.008Google ScholarDigital Library
- Dionisis Margaris and Costas Vassilakis. 2018. Exploiting rating abstention intervals for addressing concept drift in social network recommender systems. In Informatics, Vol. 5. MDPI, 21.Google Scholar
- Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, John Dickerson, and Colin White. 2022. On the Generalizability and Predictability of Recommender Systems. In Advances in Neural Information Processing Systems, Vol. 35. Curran Associates, Inc., 4416–4432.Google Scholar
- Harris Papadakis, Antonis Papagrigoriou, Costas Panagiotakis, Eleftherios Kosmas, and Paraskevi Fragopoulou. 2022. Collaborative filtering recommender systems taxonomy. Knowledge and Information Systems 64, 1 (2022), 35–74.Google ScholarDigital Library
- Yoon-Joo Park and Alexander Tuzhilin. 2008. The Long Tail of Recommender Systems and How to Leverage It. In Proceedings of the 2008 ACM Conference on Recommender Systems (Lausanne, Switzerland) (RecSys ’08). Association for Computing Machinery, New York, NY, USA, 11–18. https://doi.org/10.1145/1454008.1454012Google ScholarDigital Library
- Ladislav Peska and Peter Vojtas. 2013. Negative Implicit Feedback in E-Commerce Recommender Systems. In Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics (Madrid, Spain) (WIMS ’13). Association for Computing Machinery, New York, NY, USA, Article 45, 4 pages. https://doi.org/10.1145/2479787.2479800Google ScholarDigital Library
- Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang. 2021. A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions. ACM Comput. Surv. 54, 4, Article 76 (may 2021), 34 pages. https://doi.org/10.1145/3447582Google ScholarDigital Library
- Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (Chapel Hill, North Carolina, USA) (CSCW ’94). Association for Computing Machinery, New York, NY, USA, 175–186. https://doi.org/10.1145/192844.192905Google ScholarDigital Library
- Mario Rodriguez, Christian Posse, and Ethan Zhang. 2012. Multiple Objective Optimization in Recommender Systems. In Proceedings of the Sixth ACM Conference on Recommender Systems (Dublin, Ireland) (RecSys ’12). Association for Computing Machinery, New York, NY, USA, 11–18. https://doi.org/10.1145/2365952.2365961Google ScholarDigital Library
- Alan Said and Alejandro Bellogín. 2014. Comparative Recommender System Evaluation: Benchmarking Recommendation Frameworks. In Proceedings of the 8th ACM Conference on Recommender Systems (Foster City, Silicon Valley, California, USA) (RecSys ’14). Association for Computing Machinery, New York, NY, USA, 129–136. https://doi.org/10.1145/2645710.2645746Google ScholarDigital Library
- Oren Sar Shalom, Shlomo Berkovsky, Royi Ronen, Elad Ziklik, and Amir Amihood. 2015. Data Quality Matters in Recommender Systems. In Proceedings of the 9th ACM Conference on Recommender Systems (Vienna, Austria) (RecSys ’15). Association for Computing Machinery, New York, NY, USA, 257–260. https://doi.org/10.1145/2792838.2799670Google ScholarDigital Library
- J Ben Schafer, Joseph Konstan, and John Riedl. 1999. Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce. 158–166.Google ScholarDigital Library
- Alexey Tsymbal. 2004. The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin 106, 2 (2004), 58.Google Scholar
- Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, and Hugo Larochelle. 2017. A Meta-Learning Perspective on Cold-Start Recommendations for Items. In Advances in Neural Information Processing Systems, Vol. 30. Curran Associates, Inc.Google Scholar
- Lukas Wegmeth and Joeran Beel. 2022. CaMeLS: Cooperative Meta-Learning Service for Recommender Systems. (2022).Google Scholar
- Iordanis Xanthopoulos, Ioannis Tsamardinos, Vassilis Christophides, Eric Simon, and Alejandro Salinger. 2020. Putting the Human Back in the AutoML Loop.. In EDBT/ICDT Workshops.Google Scholar
- Eva Zangerle and Christine Bauer. 2022. Evaluating Recommender Systems: Survey and Framework. ACM Comput. Surv. 55, 8, Article 170 (dec 2022), 38 pages. https://doi.org/10.1145/3556536Google ScholarDigital Library
- Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Comput. Surv. 52, 1, Article 5 (feb 2019), 38 pages. https://doi.org/10.1145/3285029Google ScholarDigital Library
- Tunhou Zhang, Dehua Cheng, Yuchen He, Zhengxing Chen, Xiaoliang Dai, Liang Xiong, Feng Yan, Hai Li, Yiran Chen, and Wei Wen. 2023. NASRec: Weight Sharing Neural Architecture Search for Recommender Systems. In Proceedings of the ACM Web Conference 2023. ACM. https://doi.org/10.1145/3543507.3583446Google ScholarDigital Library
- Ruiqi Zheng, Liang Qu, Bin Cui, Yuhui Shi, and Hongzhi Yin. 2023. AutoML for Deep Recommender Systems: A Survey. ACM Trans. Inf. Syst. 41, 4, Article 101 (mar 2023), 38 pages. https://doi.org/10.1145/3579355Google ScholarDigital Library
- Marc-André Zöller and Marco F Huber. 2021. Benchmark and survey of automated machine learning frameworks. Journal of artificial intelligence research 70 (2021), 409–472.Google ScholarDigital Library
Index Terms
- Improving Recommender Systems Through the Automation of Design Decisions
Recommendations
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