Column-Wise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization

Balasubramaniam, Thirunavukarasu; Nayak, Richi; Yuen, Chau; Tian, Yu-Chu

doi:10.1109/tkde.2020.2967045

Cited by 9 publications

(5 citation statements)

References 40 publications

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“…In another related study, a tensor is recursively decomposed from a higher rank into a lower one until reaching the vectorized version in such a way that the forward and hierarchical multiplication of the basic terms leads to the original tensor [26]. Some of the studies concentrated on introducing and analyzing coupled tensor-matrix [27,28] and its nonnegative versions [29,30] in data fusion research. Acar among others used this concept for analyzing different types of data, e.g., multiomics data [31] and EEG multiomics [32].…”

Section: A Novel Tensor-matrix-tensor Formulationmentioning

confidence: 99%

A Comparative Analysis of Computational Drug Repurposing Approaches – Proposing a Novel Tensor-Matrix-Tensor Factorization Method

Zabihian,

Asghari,

Hooshmand

et al. 2024

Preprint

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Graphical Abstract Abstract Computational drug repurposing is a screening method for drug discovery that utilizes a wide spectrum of techniques and tools. It reduces the process cost and time of introducing drugs to the market. No need to mention that drug-related subject is a critical field, therefore, makes screening challenging and an important step for further drug investigations. This work tends to study four approaches of computational drug repurposing, i.e., factorization-based methods, machine learning methods, deep learning methods, and graph neural networks. To achieve this aim, we propose new methods for each approach and discuss their performances, challenges, advantages, and disadvantages. To cover the first approach, we propose a novel tensor-matrix-tensor (TMT) formulation as a new data array method with a gradient-based factorization procedure. To cover the second approach, we present two machine learning algorithms, i.e., SVM and random forest, with a new embedding representation. The paper compares the proposed approaches with two deep-learning methods and a graph attention network model on two datasets and discusses their results. The results show deep learning methods outperform other approaches.

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Section: A Novel Tensor-matrix-tensor Formulationmentioning

confidence: 99%

A Comparative Analysis of Computational Drug Repurposing Approaches – Proposing a Novel Tensor-Matrix-Tensor Factorization Method

Zabihian,

Asghari,

Hooshmand

et al. 2024

Preprint

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“…34. However, we can also leverage a variety of different optimization schemes including cyclic/block coordinate descent [Kim et al 2014;Rossi and Zhou 2016], stochastic gradient descent [Oh et al 2015;Yun et al 2014], among others [Balasubramaniam et al 2020;Bouchard et al 2013;Choi et al 2019;Schenker et al 2021;Singh and Gordon 2008].…”

Section: Pvisrec (Acm Only)mentioning

confidence: 99%

Personalized Visualization Recommendation

Qian¹,

Rossi²,

Du³

et al. 2021

Preprint

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Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset, and not the actual user and their past visualization feedback. These systems recommend the same visualizations for every user, despite that the underlying user interests, intent, and visualization preferences are likely to be fundamentally different, yet vitally important. In this work, we formally introduce the problem of personalized visualization recommendation and present a generic learning framework for solving it. In particular, we focus on recommending visualizations personalized for each individual user based on their past visualization interactions (e.g., viewed, clicked, manually created) along with the data from those visualizations. More importantly, the framework can learn from visualizations relevant to other users, even if the visualizations are generated from completely different datasets. Experiments demonstrate the effectiveness of the approach as it leads to higher quality visualization recommendations tailored to the specific user intent and preferences. To support research on this new problem, we release our user-centric visualization corpus consisting of 17.4k users exploring 94k datasets with 2.3 million attributes and 32k user-generated visualizations.

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“…The CTF is the common scheme which has been widely exploited for joint analysis of heterogeneous data (Acar et al 2011b). In particular, CTF has proved useful in applications where the goal is estimation of missing data such as recommendation systems (Acar et al 2015;Frolov and Oseledets 2017;Balasubramaniam et al 2020). Suppose we have the incomplete third-order tensor X ∈ ℝ I×J×K coupled with matrix Y ∈ ℝ I×M , in the first mode as in Fig.…”

Section: Preliminariesmentioning

confidence: 99%

Modeling user preference dynamics with coupled tensor factorization for social media recommendation

Tahmasbi

Jalali

Shakeri

2020

J Ambient Intell Human Comput

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An essential problem in real-world recommender systems is that user preferences are not static and users are likely to change their preferences over time. Recent studies have shown that the modelling and capturing the dynamics of user preferences lead to significant improvements on recommendation accuracy and, consequently, user satisfaction. In this paper, we develop a framework to capture user preference dynamics in a personalized manner based on the fact that changes in user preferences can vary individually. We also consider the plausible assumption that older user activities should have less influence on a user’s current preferences. We introduce an individual time decay factor for each user according to the rate of his preference dynamics to weigh the past user preferences and decrease their importance gradually. We exploit users’ demographics as well as the extracted similarities among users over time, aiming to enhance the prior knowledge about user preference dynamics, in addition to the past weighted user preferences in a developed coupled tensor factorization technique to provide top-K recommendations. The experimental results on the two real social media datasets—Last.fm and Movielens—indicate that our proposed model is better and more robust than other competitive methods in terms of recommendation accuracy and is more capable of coping with problems such as cold-start and data sparsity.

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Column-Wise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization

Cited by 9 publications

References 40 publications

A Comparative Analysis of Computational Drug Repurposing Approaches – Proposing a Novel Tensor-Matrix-Tensor Factorization Method

A Comparative Analysis of Computational Drug Repurposing Approaches – Proposing a Novel Tensor-Matrix-Tensor Factorization Method

Personalized Visualization Recommendation

Modeling user preference dynamics with coupled tensor factorization for social media recommendation

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