Flexible Mixture Model for Collaborative Filtering

By Luo Si, Rong Jin. International Conference on Machine Learning, 2003

355 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/88afb9b7826c27363c9d3b59b12ab9559eb4758a

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Citations in the corpus

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].”
Social Recommendation Using Low-Rank Semidefinite Program (2011, methodology): “On the other hand, the model-based approaches (Hofmann 2003; Zhang and Koren 2007; Si and Jin 2003) aim to take advantage of the statistical and machine learning techniques to train a compact model from the rating data.”
Transfer Learning in Collaborative Filtering for Sparsity Reduction (2010, unknown): “Collaborative Filtering (CF) (Resnick et al. 1994) was proposed to predict the missing values in an incomplete matrix , i.e. user-item rating matrix.”
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].”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, methodology): “Similarly, Si and Jin [99] also use probabilistic latent semantic analysis to propose a flexible mixture model that allows modeling the classes of users and items explicitly with two sets of latent variables.”
Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “…between items; the latent semantic model [Hofmann 2004], which clusters users and items around latent classes of U-I interactions; and the mixture model [Si and Jin 2003; Kleinberg and Sandler 2008], which models probability distributions of items within each cluster of like-minded users.”
TFMAP: optimizing MAP for top-n context-aware recommendation (2012, background): “, latent semantic models [12, 28], and matrix factorization models, which learns a latent feature/factor vector for each user and item in the dataset such that the inner product of these features minimizes an explicit or implicit loss function [5].”
Time weight collaborative filtering (2005, background): “…Information Filtering ; I.2.6 [Arti.cial Intelligence]: Learning Knowledge Acquisition General Terms Algorithms, Experimentation, Performance, Human Factors Keywords collaborative .ltering, item-based approach, time weight Permission to make digital or hard copies of all or part of this…”
A Survey of Collaborative Filtering Techniques (2009, methodology): “The major difference between CF and content-based recommender systems is that CF only uses the user-item ratings data to make predictions and recommendations, while content-based recommender systems rely on the features of users and items for predictions [15].”
An automatic weighting scheme for collaborative filtering (2004, methodology): “Finally, model-based approaches tend to perform worse than the memory-based approaches when the number of training users is small [15].”
Learning to recommend with social trust ensemble (2009, methodology): “Examples of model-based approaches include aspect models [7, 8, 21], the latent factor model [4], the Bayesian hierarchical model [24] and the ranking model [11].”
Transfer learning for collaborative filtering via a rating-matrix generative model (2009, methodology): “The most similar one is the ﬂexible mixture model (FMM) (Si & Jin, 2003) which simultaneously models users and items into mixture models in terms of two latent cluster variables.”
Personalized active learning for collaborative filtering (2008, methodology): “The Flexible Mixture Model (FMM) [4], is a modi.ed version of the Aspect Model in which there are two layers of latent aspects {zu,zm}, one grouping the users with similar interests (zu) and one grouping the items with similar patrons (zm).”
Cross-Domain Recommendation via Cluster-Level Latent Factor Model (2013, background): “– FMM (Flexible Mixture Model) based model [10]: a single-domain model which uses probabilistic mixture model to learn latent cluster structure in each single domain and then provide the single domain performance separately.”
Transfer learning in heterogeneous collaborative filtering domains (2013, methodology): “codebook sharing methods of CBT [31] and RMGM [32] can be considered as adaptive and collective extensions of [50,19].”
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].”
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques (2012, methodology): “Examples of such techniques include Bayesian clustering, aspect model, flexible mixture model, matrix factorization, and other methods [4], [5], [9], [25], [44], [48].”