Local Low-Rank Matrix Approximation

By Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer. 2013

216 citations recorded by the source corpus.

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Citations in the corpus

A Neural Autoregressive Approach to Collaborative Filtering (2016, methodology): “We use a single hidden layer architecture and the number of hidden units is set to 500 , same as AutoRec (Sedhain et al., 2015) and LLORMA (Lee et al., 2013).”
UvA-DARE ( Digital Academic Repository ) Graph Convolutional Matrix Completion (2017, unknown): “MovieLens 1M and 10M We compare against current state-of-the-art collaborative filtering algorithms, such as AutoRec [27], LLorma [17], and CF-NADE [32].”
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems (2018, methodology): “ product of two latent vectors, thus it does not require the co-occurrence of user and item in the training set. MF is the most popular model-based collaborative filtering method in the RS literature [17, 20, 30, 38]. [4, 28] extend MF to leveraging side information, in which both a linear model and”
AutoRec: Autoencoders Meet Collaborative Filtering (2015, methodology): “Owing to the Netﬂix challenge, a panoply of diﬀerent CF models have been proposed, with popular choices being matrix factori-sation [1, 2] and neighbourhood models [5].”
Improving Content-based and Hybrid Music Recommendation using Deep Learning (2014, background): “Lee et al. [35] used a convolutional deep belief network to extract features in an unsupervised fashion for tasks such as music genre classiﬁcation.”
Toward Implicit Sample Noise Modeling: Deviation-driven Matrix Factorization (2016, methodology): “The experiments are conducted using the protocol proposed in the state-of-the-art solutions (Lee et al. 2013; Sedhain et al. 2015).”
Deep Models of Interactions Across Sets (2018, unknown): “…GRALS (Rao et al., 2015) 1.313 0.833 38.0 - sRGCNN (Monti et al., 2017) 1.179 0.801 22.4 - GC-MC (Berg et al., 2017) 0 (Mnih & Salakhutdinov, 2008) - - - 0.897 LLORMA-Local (Lee et al., 2013) - - - 0.834 I-AUTOREC (Sedhain et al., 2015) - - - 0.823 CF-NADE (Zheng et al., 2016) - - - 0.803 Table 4.”
Latent Cross: Making Use of Context in Recurrent Recommender Systems (2018, background): “effort, there is a large body of research on collaborative filtering and recommendation algorithms, including matrix factorization during the Netflix Prize [24, 28, 30, 35], local focused models [5, 9, 31], and more recently deep learning [11, 36].”
A Harmonic Extension Approach for Collaborative Ranking (2016, methodology): “(17) This is the same optimization problem we solved in Section 2 and Section 3. 7 5 Weight Matrix The weight matrix W plays an important role in our algorithm as well as other graph-based approaches [24, 18, 10, 7]. We employ the typical user-user or item-item graph with cosine similarity used in e”
Interleaved Structured Sparse Convolutional Neural Networks (2018, background): “Low-rank matrix (tensor) multiplication or decomposition has been widely studied in matrix analysis [19, 20] and applied to network compression and network architecture design.”
Neural Network Matrix Factorization (2015, methodology): “For every fixed setting of λ, the network and features were learned by optimizing Eq.”
Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs (2015, methodology): “Newer algorithms were explored to face those constraints such as Non Linear Probabilistic Matrix Factorization (NL-PMF) [8], Local Low Rank Matrix Approximation (LLORMA) [10] or Factorization Machines (FM) [13] which are more ﬂexible extensions of this approach.”