Relational learning via collective matrix factorization

By Ajit Paul Singh, Geoffrey J. Gordon. 2008

1,416 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Graph Learning

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In Graph Learning

Citations in the corpus

On Sampling Strategies for Neural Network-based Collaborative Filtering (2017, methodology): “To address the issue and improve performance, hybrid methods are proposed to incorporate side information [5, 7, 25, 28, 38], as well as content information [4, 11, 31, 32].”
Collaborative Deep Learning for Recommender Systems (2014, methodology): “The models included in our comparison are listed as follows: • CMF: Collective Matrix Factorization [30] is a model incorporating different sources of information by simultaneously factorizing multiple matrices.”
A Comprehensive Survey on Transfer Learning (2019, methodology): “The proposed approach is based on the collective matrix factorization technique [177], where a factor matrix is shared across domains.”
Embedding Entities and Relations for Learning and Inference in Knowledge Bases (2014, background): “For instance, Freebase contains millions of entities and billions of facts (triples) involving a large variety of predicates (relation types).”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, positive): “s to overcome the data sparsity. Zhu et al. [279] enrich the representations of the target images with semantic concepts extracted from the intermediate data through a Collective Matrix Factorization [208]. They show that these generated latent semantic features allow to build a better image classiﬁ”
A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems (2017, methodology): “Various models show that additional side information can act as a useful informative prior that can significantly improve results (Porteous, Asuncion, and Welling 2010; Singh and Gordon 2008).”
Text Is All You Need: Learning Language Representations for Sequential Recommendation (2023, neutral): “Previous methods for knowledge transfer mainly rely on shared information between the source and target domains including common users [13, 31, 32, 35], items [26, 39] or attributes [29].”
SSE: Semantically Smooth Embedding for Knowledge Graphs (2017, background): “This section briefly reviews two lines of related work: KG embedding and manifold learning.”
Label Informed Attributed Network Embedding (2017, background): “Users in the same group tend to share posts or photos of similar themes, and they also frequently interact with each other.”
Instilling Social to Physical: Co-Regularized Heterogeneous Transfer Learning (2016, methodology): “2011) that follows Collective Matrix Factorization (Singh and Gordon 2008), and HeMap (Shi et al.”
Poisson Noise Reduction with Non-local PCA (2012, background): “Some more specific details can be found in [39, 40] about matrix factorization for exponential families.”
All-at-once Optimization for Coupled Matrix and Tensor Factorizations (2011, background): “This formulation is a special case of the general approach introduced in [28], which extends to diﬀerent loss functions.”
FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop (2014, background): “[17] jointly factorize two related matrices for better decomposition and provide stochastic updates for this coupled MF optimization.”