Eigentaste: A constant time collaborative filtering algorithm. Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued user ratings on a common set of items and applies Principal Component Analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clustering of users and rapid computation of recommendations. For a database of $n$ users, standard nearest-neighbor techniques require $O(n)$ processing time to compute recommendations, whereas Eigentaste requires $O(1)$ (constant) time. We compare Eigentaste to alternative algorithms using data from Jester, an online joke recommending system.par Jester has collected approximately 2,500,000 ratings from 57,000 users. We use the Normalized Mean Absolute Error (NMAE) measure to compare performance of different algorithms. In the appendix we use uniform and normal distribution models to derive analytic estimates of NMAE when predictions are random. On the Jester dataset, Eigentaste computes recommendations two orders of magnitude faster with no loss of accuracy. Jester is online at: http://eigentaste.berkeley.edu.

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  1. Ongie, Greg; Pimentel-Alarcón, Daniel; Balzano, Laura; Willett, Rebecca; Nowak, Robert D.: Tensor methods for nonlinear matrix completion (2021)
  2. Sagan, April; Mitchell, John E.: Low-rank factorization for rank minimization with nonconvex regularizers (2021)
  3. Filos-Ratsikas, Aris; Micha, Evi; Voudouris, Alexandros A.: The distortion of distributed voting (2020)
  4. Rohan Anand, Joeran Beel: Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization (2020) arXiv
  5. Del Corso, Gianna M.; Romani, Francesco: Adaptive nonnegative matrix factorization and measure comparisons for recommender systems (2019)
  6. Feng, Yuehua; Xiao, Jianwei; Gu, Ming: Flip-flop spectrum-revealing QR factorization and its applications to singular value decomposition (2019)
  7. Guo, Taolin; Luo, Junzhou; Dong, Kai; Yang, Ming: Locally differentially private item-based collaborative filtering (2019)
  8. Jin, Zheng-Fen; Wan, Zhongping; Zhang, Zhiyong: Strictly contractive Peaceman-Rachford splitting method to recover the corrupted low rank matrix (2019)
  9. Nath, Swaprava; Sandholm, Tuomas: Efficiency and budget balance in general quasi-linear domains (2019)
  10. Sato, Hiroyuki; Kasai, Hiroyuki; Mishra, Bamdev: Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport (2019)
  11. Bi, Xuan; Qu, Annie; Shen, Xiaotong: Multilayer tensor factorization with applications to recommender systems (2018)
  12. Khetan, Ashish; Oh, Sewoong: Generalized rank-breaking: computational and statistical tradeoffs (2018)
  13. Negahban, Sahand; Oh, Sewoong; Thekumparampil, Kiran K.; Xu, Jiaming: Learning from comparisons and choices (2018)
  14. Shi, Xiaoyu; Shang, Ming-Sheng; Luo, Xin; Khushnood, Abbas; Li, Jian: Long-term effects of user preference-oriented recommendation method on the evolution of online system (2017)
  15. Zhang, Zhipeng; Kudo, Yasuo; Murai, Tetsuya: Neighbor selection for user-based collaborative filtering using covering-based rough sets (2017)
  16. Adomavicius, Gediminas; Zhang, Jingjing: Classification, ranking, and top-K stability of recommendation algorithms (2016) ioport
  17. Carmel, Yuval; Patt-Shamir, Boaz: Comparison-based interactive collaborative filtering (2016)
  18. Hautamäki, Antti: Points of view: a conceptual space approach (2016)
  19. Jin, Zheng-Fen; Wan, Zhongping; Jiao, Yuling; Lu, Xiliang: An alternating direction method with continuation for nonconvex low rank minimization (2016)
  20. Nath, Swaprava; Sandholm, Tuomas: Efficiency and budget balance (2016)

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