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 42 articles )

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  1. Adomavicius, Gediminas; Zhang, Jingjing: Classification, ranking, and top-K stability of recommendation algorithms (2016) ioport
  2. Carmel, Yuval; Patt-Shamir, Boaz: Comparison-based interactive collaborative filtering (2016)
  3. Jin, Zheng-Fen; Wan, Zhongping; Jiao, Yuling; Lu, Xiliang: An alternating direction method with continuation for nonconvex low rank minimization (2016)
  4. Boutilier, Craig; Caragiannis, Ioannis; Haber, Simi; Lu, Tyler; Procaccia, Ariel D.; Sheffet, Or: Optimal social choice functions: a utilitarian view (2015)
  5. 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
  6. Geng, Juan; Wang, Laisheng; Wang, Yanfei: A non-convex algorithm framework based on DC programming and DCA for matrix completion (2015)
  7. Nguyen, Duc Anh; Duong, Trong Hai: Video recommendation using neuro-fuzzy on social TV environment (2015) ioport
  8. Wang, Zheng; Lai, Ming-Jun; Lu, Zhaosong; Fan, Wei; Davulcu, Hasan; Ye, Jieping: Orthogonal rank-one matrix pursuit for low rank matrix completion (2015)
  9. Bobadilla, Jesús; Hernando, Antonio; Ortega, Fernando; Gutiérrez, Abraham: Collaborative filtering based on significances (2012) ioport
  10. Chen, Caihua; He, Bingsheng; Yuan, Xiaoming: Matrix completion via an alternating direction method (2012)
  11. Langseth, Helge; Nielsen, Thomas Dyhre: A latent model for collaborative filtering (2012) ioport
  12. Wang, Hsiao-Fan; Wu, Cheng-Ting: A strategy-oriented operation module for recommender systems in E-commerce (2012) ioport
  13. Wen, Zaiwen; Yin, Wotao; Zhang, Yin: Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm (2012)
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  15. Nisgav, Aviv; Patt-Shamir, Boaz: Finding similar users in social networks (2011)
  16. Barragáns-Martínez, Ana Belén; Costa-Montenegro, Enrique; Burguillo, Juan C.; Rey-López, Marta; Mikic-Fonte, Fernando A.; Peleteiro, Ana: A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition (2010) ioport
  17. Davis, Darcy A.; Chawla, Nitesh V.; Christakis, Nicholas A.; Barabási, Albert-László: Time to CARE: a collaborative engine for practical disease prediction (2010) ioport
  18. Toh, Kim-Chuan; Yun, Sangwoon: An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems (2010)
  19. Yakut, Ibrahim; Polat, Huseyin: Privacy-preserving SVD-based collaborative filtering on partitioned data (2010)
  20. Yang, Qiang: Three challenges in data mining (2010) ioport

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