Eigentaste

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.


References in zbMATH (referenced in 60 articles )

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  1. Filos-Ratsikas, Aris; Micha, Evi; Voudouris, Alexandros A.: The distortion of distributed voting (2020)
  2. Rohan Anand, Joeran Beel: Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization (2020) arXiv
  3. Del Corso, Gianna M.; Romani, Francesco: Adaptive nonnegative matrix factorization and measure comparisons for recommender systems (2019)
  4. Feng, Yuehua; Xiao, Jianwei; Gu, Ming: Flip-flop spectrum-revealing QR factorization and its applications to singular value decomposition (2019)
  5. Guo, Taolin; Luo, Junzhou; Dong, Kai; Yang, Ming: Locally differentially private item-based collaborative filtering (2019)
  6. Nath, Swaprava; Sandholm, Tuomas: Efficiency and budget balance in general quasi-linear domains (2019)
  7. Sato, Hiroyuki; Kasai, Hiroyuki; Mishra, Bamdev: Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport (2019)
  8. Bi, Xuan; Qu, Annie; Shen, Xiaotong: Multilayer tensor factorization with applications to recommender systems (2018)
  9. Khetan, Ashish; Oh, Sewoong: Generalized rank-breaking: computational and statistical tradeoffs (2018)
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  11. 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)
  12. Zhang, Zhipeng; Kudo, Yasuo; Murai, Tetsuya: Neighbor selection for user-based collaborative filtering using covering-based rough sets (2017)
  13. Adomavicius, Gediminas; Zhang, Jingjing: Classification, ranking, and top-K stability of recommendation algorithms (2016) ioport
  14. Carmel, Yuval; Patt-Shamir, Boaz: Comparison-based interactive collaborative filtering (2016)
  15. Hautamäki, Antti: Points of view: a conceptual space approach (2016)
  16. Jin, Zheng-Fen; Wan, Zhongping; Jiao, Yuling; Lu, Xiliang: An alternating direction method with continuation for nonconvex low rank minimization (2016)
  17. Nath, Swaprava; Sandholm, Tuomas: Efficiency and budget balance (2016)
  18. Boutilier, Craig; Caragiannis, Ioannis; Haber, Simi; Lu, Tyler; Procaccia, Ariel D.; Sheffet, Or: Optimal social choice functions: a utilitarian view (2015)
  19. Carmel, Yuval; Patt-Shamir, Boaz: Comparison-based interactive collaborative filtering (2015)
  20. Dikow, Heidi; Hasan, Omar; Kosch, Harald; Brunie, Lionel; Sornin, Renaud: Improving the accuracy of business-to-business (B2B) reputation systems through rater expertise prediction (2015) ioport

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