Eigentaste: A Constant Time Collaborative Filtering Algorithm

By Ken Goldberg, Theresa Roeder, Dhruv Gupta, Chris Perkins. 2001

1,688 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

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Citations in the corpus

StarSpace: Embed All The Things! (2017, methodology): “In the domain of recommendation, embedding models have had a large degree of success, starting from SVD (Goldberg et al. 2001) and its improvements such as SVD++ (Koren and Bell 2015), as well as a host of other techniques, e.”
Relative-Error CUR Matrix Decompositions (2007, methodology): “The so-called Jester joke dataset is a commonly-used benchmark for recommendation system research and development [ 35 ].”
Computational and Statistical Tradeoffs in Learning to Rank (2016, methodology): “On sushi preferences (Kamishima, 2003) and jester dataset (Goldberg et al., 2001), we improve over pairwise breaking and achieves same performance as the oracle MLE. Full rankings over κ = 10 types of sushi are randomly chosen from d = 100 types of sushi are provided by n = 5000 individuals. As the ”
Functional matrix factorizations for cold-start recommendation (2011, methodology): “Several models such as [9,19,20] constructed the interview process with a static seed set selected based on measures such as informativeness, popularity and coverage.”
Recommender Systems (2016, positive): “Aside from their use in factorbased models, dimensionality reduction methods are also used to reduce the dimensionality of the ratings matrix to improve the efficiency of the user-to-user or item-to-item similarity in the reduced space [228, 525].”
A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method (2016, methodology): “1 M Data Set): Collected through the joke-recommender Jester [46], and contains 1 186 324 continuous ratings in the scale of [−10, 10] from 24 983 users on 100 jokes.”
Privacy-preserving collaborative filtering using randomized perturbation techniques (2003, methodology): “A constant time collaborative filtering algorithm was proposed by [8], which is based on principal component analysis (PCA).”
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms (2020, background): “Dataset #Users #Items #Interactions Reference MovieLens Harper and Konstan (2016) Anime 73,515 11,200 7,813,737 Kaggle (2016a) Epinions 116,260 41,269 188,478 Zhao et al.”
ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines (2007, background): “, 2002], Ringo for music [Shardanand and Maes, 1995], Jester for jokes [Goldberg et al., 2001] and many other (see e.”
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, methodology): “We test this model on the Jester dataset, a repository of ratings from 24,983 users on a set of 100 jokes (Goldberg et al., 2001).”
Modeling User Rating Profiles For Collaborative Filtering (2003, neutral): “[6].”
Shilling recommender systems for fun and profit (2004, background): “In addition, numerous recommenders in a variety of domains have been developed for research purposes, including GroupLens (Usenet news) [11], Ringo (music) [20], and Jester (jokes) [7].”
Scalable collaborative filtering using cluster-based smoothing (2005, methodology): “Principle Component Analysis (PCA) [8] and information retrieval techniques such as Latent Semantic Indexing (LSI) [7][18] are also proposed.”