Journal ArticleDOI
Multi-Sided recommendation based on social tensor factorization
Minsung Hong,Jason J. Jung +1 more
TLDR
The new model social tensor is introduced to propose a tensor-based recommendation with a social relationship to deal with the existing problems and the ability of the method to improve the recommendation performance, even in the case of a new user.About:
This article is published in Information Sciences.The article was published on 2018-06-01. It has received 44 citations till now. The article focuses on the topics: Tensor (intrinsic definition) & Recommender system.read more
Citations
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Journal Article
Air-Pollution Prediction in Smart Cities through Machine Learning Methods: A Case of Study in Murcia, Spain.
Raquel Martínez-España,Andrés Bueno-Crespo,Isabel Maria Timon-Perez,Jesús Soto,Andrés Muñoz,José M. Cecilia +5 more
TL;DR: The results indicate an adjustment for the proposed O3 prediction models from 90% and a root mean square error less than 11 μ/m for the cities of the Region of Murcia involved in the study.
Journal ArticleDOI
A novel hybrid deep recommendation system to differentiate user’s preference and item’s attractiveness
TL;DR: A novel approach which is essentially a hybrid probabilistic matrix factorization model, which tries to model item’s textual attractiveness to different users via a proposed attention based convolutional neural network and optimize these two sub components under a unified framework is proposed.
Journal ArticleDOI
Multi-criteria tensor model for tourism recommender systems
Minsung Hong,Jason J. Jung +1 more
TL;DR: The comparative analysis of several variants of the proposed models showed that considering Western and Eastern cultures is appropriate for improving predictive performances and their stability.
Journal ArticleDOI
Design of Momentum Fractional Stochastic Gradient Descent for Recommender Systems
TL;DR: The proposed mF-SGD method is shown to offer improved estimation accuracy and convergence rate, as compared to F- SGD and standard momentum SGD for different proportions of previous gradients, fractional orders, learning rates and number of features.
Journal ArticleDOI
Iterative p-shrinkage thresholding algorithm for low Tucker rank tensor recovery
TL;DR: Numerical results on simulation data demonstrate that the proposed iterative p-shrinkage thresholding algorithm can successfully recover varieties of synthetic low Tucker rank tensors in different sampling ratios with better quality compared to the existing state-of-the-art tensor recovery algorithms.
References
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Posted Content
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
Proceedings Article
Empirical analysis of predictive algorithms for collaborative filtering
TL;DR: Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.
Proceedings ArticleDOI
Recommender systems with social regularization
TL;DR: This paper proposes a matrix factorization framework with social regularization, which can be easily extended to incorporate other contextual information, like social tags, etc, and demonstrates that the approaches outperform other state-of-the-art methods.
Proceedings ArticleDOI
Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering
TL;DR: This work introduces a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factorization that allows for a flexible and generic integration of contextual information by modeling the data as a User-Item-Context N-dimensional tensor instead of the traditional 2D User- Item matrix.
Journal ArticleDOI
Social big data
TL;DR: This paper presents a revision of the new methodologies that are designed to allow for efficient data mining and information fusion from social media and of thenew applications and frameworks that are currently appearing under the “umbrella” of the social networks, socialMedia and big data paradigms.