Orthogonal Inductive Matrix Completion

Ledent, Antoine; Alves, Rodrigo; Kloft, Marius

doi:10.1109/tnnls.2021.3106155

Cited by 5 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The intuition behind this family of algorithms is to learn mappings from the feature space to the latent factor space, such that inductive matrix completion methods can adapt to new rows and columns without retraining. However, it has been recently shown (Zhang & Chen, 2020;Ledent et al, 2021;Wu et al, 2021) that inductive matrix completion methods provide limited performance due to the inferior expressiveness of the feature space. On the other hand, the prediction accuracy has strong constraints on the content quality, but in practice the high quality content is becoming hard to collect due to legal risks (Voigt & Von dem Bussche, 2017).…”

Section: A Related Workmentioning

confidence: 99%

“…Another emerging line of research has focused on learning the mapping from side information (or content features) to latent factors (Jain & Dhillon, 2013;Xu et al, 2013;Ying et al, 2018;Zhong et al, 2019). However, it has been recently shown (Zhang & Chen, 2020;Ledent et al, 2021;Wu et al, 2021) that in general this family of algorithms would possibly suffer inferior expressiveness when high-quality content is not available. Further, collecting personal data is likely to be unlawful as well as a breach of the data minimization principle in GDPR (Voigt & Von dem Bussche, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution

Chen¹,

Geng²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros. We propose a unified graph signal sampling framework which enjoys the benefits of graph signal analysis and processing. The key idea is to transform each user's ratings on the items to a function (graph signal) on the vertices of an item-item graph, then learn structural graph properties to recover the function from its values on certain vertices -the problem of graph signal sampling. We propose a class of regularization functionals that takes into account discrete random label noise in the graph vertex domain, then develop the GS-IMC approach which biases the reconstruction towards functions that vary little between adjacent vertices for noise reduction. Theoretical result shows that accurate reconstructions can be achieved under mild conditions. For the online setting, we develop a Bayesian extension, i.e., BGS-IMC which considers continuous random Gaussian noise in the graph Fourier domain and builds upon a predictioncorrection update algorithm to obtain the unbiased and minimum-variance reconstruction. Both GS-IMC and BGS-IMC have closed-form solutions and thus are highly scalable in large data as verified on public benchmarks.

show abstract

Section: A Related Workmentioning

confidence: 99%