Recommender systems

Not my area, but I need a landing page to refer to for some non-specialist contacts of mine.

I am most familiar with the matrix factorization approaches (e.g. factorization machines, NNMF) but there are many, e.g. variational autoencoder approaches are en vogue.

An overview by Javier lists many approaches.

  1. Most Popular recommendations (the baseline)
  2. Item-User similarity based recommendations
  3. kNN Collaborative Filtering recommendations
  4. GBM based recommendations
  5. Non-Negative Matrix Factorization recommendations
  6. Factorization Machines (Steffen Rendle 2010)
  7. Field Aware Factorization Machines (Yuchin Juan, et al, 2016)
  8. Deep Learning based recommendations (Wide and Deep, Heng-Tze Cheng, et al, 2016)
  9. Neural Collaborative Filtering (Xiangnan He et al., 2017)
  10. Neural Graph Collaborative Filtering (Wang Xiang et al. 2019)
  11. Variational Autoencoders for Collaborative Filtering (Dawen Liang et al,. 2018)

Quentin Bacquet curtly summarises some methods and their performance on a problem, and more deeply introduces the VAE method with an implementation.

TBD: vowpal wabbit can do fast recommendations, but the manual is abstruse. Link to the preferred method and make some vague recommendations.


Abernethy, Jacob, Francis Bach, Theodoros Evgeniou, and Jean-Philippe Vert. 2009. “A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization.” Journal of Machine Learning Research 10: 803–26.
Heckerman, David, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, and Carl Kadie. 2000. “Dependency Networks for Inference, Collaborative Filtering, and Data Visualization.” Journal of Machine Learning Research 1: 49–75.
Koren, Yehuda, Robert Bell, and Chris Volinsky. 2009. “Matrix Factorization Techniques for Recommender Systems.” Computer 42 (8): 30–37.
Li, Lihong, Wei Chu, John Langford, and Xuanhui Wang. 2011. “Unbiased Offline Evaluation of Contextual-Bandit-Based News Article Recommendation Algorithms.” In Proceedings of the Fourth International Conference on Web Search and Web Data Mining (WSDM-11), 297–306.
Liang, Dawen, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. “Variational Autoencoders for Collaborative Filtering.” In WWW.
Sharma, Amit, Jake M. Hofman, and Duncan J. Watts. 2015. “Estimating the Causal Impact of Recommendation Systems from Observational Data.” Proceedings of the Sixteenth ACM Conference on Economics and Computation - EC ’15, 453–70.
Wang, Xinxi, and Ye Wang. 2014. “Improving Content-Based and Hybrid Music Recommendation Using Deep Learning.” In Proceedings of the 22Nd ACM International Conference on Multimedia, 627–36. MM ’14. New York, NY, USA: ACM.
Xia, Min, Xianwen Yu, Xiaoning Zhang, and Yang Cao. 2019. VAEGAN: A Collaborative Filtering Framework Based on Adversarial Variational Autoencoders,” 4206–12.
Yu, Hsiang-Fu, Cho-Jui Hsieh, Si Si, and Inderjit S. Dhillon. 2012. “Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems.” In IEEE International Conference of Data Mining, 765–74.
———. 2014. “Parallel Matrix Factorization for Recommender Systems.” Knowledge and Information Systems 41 (3): 793–819.

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