Results 31 to 40 of about 24,916,518 (300)
Increasing Performance of Recommender Systems by Combining Deep Learning and Extreme Learning Machine [PDF]
Journal of Artificial Intelligence and Data Mining, 2022 Nowadays, with the expansion of the internet and its associated technologies, recommender systems have become increasingly common. In this work, the main purpose is to apply new deep learning-based clustering methods to overcome the data sparsity problem
Z. Nazari, H.R. Koohi, J. Mousavi
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Constructing measures of sparsity
, 2022 This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsity that is as broad as possible, so that it generates all the various measures that are useful in practice, but narrow enough that the fundamental ...
Jäntti, Riku +7 more
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Sparsity regularization for image reconstruction with Poisson data [PDF]
SPIE Proceedings, 2009 This work investigates three penalized-likelihood expectation maximization (EM) algorithms for image reconstruction with Poisson data where the images are known ap riorito be sparse in the space domain. The penalty functions considered are the 1 norm, the 0 “norm,” and a penalty function based on the sum of logarithms of pixel values, R(x )= n p ...
Lingenfelter, Daniel J. +2 more
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Estimation of functional sparsity in nonparametric varying coefficient models for longitudinal data analysis [PDF]
, 2020 We study the simultaneous domain selection problem for varying coefficient models as a functional regression model for longitudinal data with many covariates.
Tu, Catherine Y. +2 more
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Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis
IEEE Access, 2021 Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications.
Muhammad Z. Ali +4 more
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Cybernetics and Information Technologies, 2023 One of the methods most frequently used to recommend films is collaborative filtering. We examine the potential of collaborative filtering in our paper’s discussion of product suggestions.
Amutha S., Vikram Surya R.
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Solving the data sparsity problem in destination prediction [PDF]
The VLDB Journal, 2014 Destination prediction is an essential task for many emerging location-based applications such as recommending sightseeing places and targeted advertising according to destinations. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories.
Andy Yuan Xue +5 more
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Tensor-based sparsity order estimation for big data applications
, 2022 S.648-652In Big Data Processing we typically face very large data sets that are highly structured. To save the computation and storage cost, it is desirable to extract the essence of the data from a reduced number of observations.
Liu, Kefei +6 more
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RTN-GNNR: Fusing Review Text Features and Node Features for Graph Neural Network Recommendation
IEEE Access, 2022 Recent recommendation systems have achieved good results by applying Graph Neural Network (GNN) to user-item interaction graphs. However, these recommendation systems can only handle structured interaction data and cannot handle unstructured review text ...
Bohuai Xiao +3 more
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Exploiting high-order behaviour patterns for cross-domain sequential recommendation
Connection Science, 2022 The cross-domain sequential recommendation aims to predict the next item based on a sequence of recorded user behaviours in multiple domains. We propose a novel Cross-domain Sequential Recommendation approach with Graph-Collaborative Filtering (CsrGCF ...
Bingyuan Wang +6 more
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