Collaborative filtering with privacy via factor analysis

By John F. Canny. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002

597 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/e032a6e6879ace7b7a790901b535165cb64ba028

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Citations in the corpus

Restricted Boltzmann machines for collaborative filtering (2007, background): “A variety of models have recently been proposed for minimizing the loss corresponding to a speci(cid:12)c probabilistic model (Hofmann, 1999; Canny, 2002; Marlin & Zemel, 2004).”
Privacy-preserving collaborative filtering using randomized perturbation techniques (2003, methodology): “1 Privacy-Preserving Collaborative Filtering Problem Today’s CF systems have a number of disadvantages [4, 5].”
Learning from Incomplete Ratings Using Non-negative Matrix Factorization (2006, methodology): “It is shown in [3] that a low-dimensional linear model of user preferences is powerful, and why this approach has been popular for CF algorithms.”
Collaborative Filtering: A Machine Learning Perspective (2004, unknown): “However, Canny is still able to find updates in closed form [13].”
Probabilistic factor models for web site recommendation (2011, methodology): “Algorithms in this category include the clustering model [12], the aspect models [7, 8, 25], the latent factor model [3], the Bayesian hierarchical model [31] and the ranking model [18].”
Collaborative filtering with temporal dynamics (2009, methodology): “1 The anatomy of a factor model One of the more successful approaches to CF is based on a matrix factorization model [2, 5, 10, 13, 18].”
Recommender Systems (2016, unknown): “Privacy-preserving techniques may include the use of perturbation techniques [35, 38, 484, 485], group-based anonymization methods [27, 352, 386, 521], or distributed methods [75, 133, 334, 551, 606].”
Recommender systems with social regularization (2011, methodology): “Algorithms in this category include the clustering model [13], the aspect models [8, 9, 33], the latent factor model [5], the Bayesian hierarchical model [40] and the ranking model [19].”
Advances in Collaborative Filtering (2011, background): “Hence, more recent works [4, 6, 10, 17, 23, 24, 28] suggested modeling directly only the observed ratings, while avoiding overfitting through an adequate regularized model.”
QoS-Aware Web Service Recommendation via Collaborative Filtering (2014, methodology): “Examples include clustering model [33], latent semantic models [11, 12] and latent factor model [5].”
Collaborative filtering with temporal dynamics (2010, methodology): “One of the more successful approaches to CF is based on a matrix factorization model [2, 5 , 10, 13, 18].”
Recommender systems: from algorithms to user experience (2012, methodology): “…MovieLens movie recommender system; with Kautz et al.’s ReferralWeb system for recommending people based on social networks and expertise Kautz et al. (1997); with Burke et al.’s FindMe knowledge-based recommenders (Burke et al. 1997), including the Entreé restaurant recommender; and many others.”
Shilling recommender systems for fun and profit (2004, methodology): “Canny [3] presents a system in which user preferences”
Collaborative Filtering Recommender Systems (2007, background): “To protect against this, researchers have developed security techniques building on encryption and shared keys [11].”
SoRec: social recommendation using probabilistic matrix factorization (2008, methodology): “Examples of model-based approaches include the clustering model [25], aspect models [8, 9, 20] and the latent factor model [3].”
Evaluating collaborative filtering recommender systems (2004, unknown): “Examples include Canny’s [2002] factor analysis algorithm, Domingos and Richardson’s [2003] algorithm for computing network value, and Pennock et al’s [2000] work on recommending through personality diagnosis algorithms.”