SVD Model Research Articles

Product recommendation for online sales systems based on transaction sessions

The advancement of information technology has influenced and accelerated the growth of many economic fields and industries, including e-commerce. This is one area of information technology that has rapidly developed and gained widespread popularity due to the significant benefits it offers to the community. Today, the need for online shopping is increasing because of its convenience and timesaving, especially for busy people. Historical session data plays an important role in helping sales systems recommend orders to customers that suit their personal preferences. This study proposes models for recommending products on online sales systems based on transaction sessions using memory-based collaborative filtering methods, including user-based and item-based, and model-based collaborative filtering methods including SVD and KNN. The experimental results show that the SVD model has a better rating prediction performance than other techniques. Therefore, it is probably proposed for product recommendations on online sales systems.

Towards a word similarity gold standard for Akkadian: creation and model optimization

Abstract We present a word similarity gold standard for Akkadian, a language documented in ancient Mesopotamian sources from the 24th century BCE until the first century CE. The gold standard comprises 300 word pairs ranked by their paradigmatic similarity by five independently working Assyriologists. We use the gold standard to tune PMI + SVD and fastText models to improve their performance. We also present a hyper-parametrized PMI + SVD model for building count-based word embeddings, that aims to deal with the data sparsity and repetition issues encountered in Akkadian texts. Our model combines Dirichlet smoothing with context distribution smoothing, and uses context similarity weighting to down-sample distortion caused by formulaic litanies and partially or fully duplicated passages.

Research on the Application of Knowledge Mapping Technology in Vocational Skills Training Demand Forecasting and Curriculum Designing

Abstract Due to the differences in individual qualifications, training duration, etc., the current vocational skills training is less efficient and cannot rationally arrange the training process and course content design etc. In this paper, we have constructed a model for predicting vocational skills training demand using knowledge mapping and knowledge tracking technology. First, a matrix is constructed to obtain the implicit relationship between knowledge. Reduce the influence weights with time intervals according to the theory of the forgetting curve. Modify the alignment scores of the attention mechanism to pay more attention to the relevant knowledge interactions in the sequence determined by the relationship coefficient g. Comparative analysis of the model and practical application reveals that the model in this paper has a better classification effect and better reflects the deeper recursive relationship and structure between knowledge points. When the professional teacher recommends the knowledge point C, the SVD model predicts the training demand of the trainees only 72.33%, while the model of this paper predicts 94.17% accuracy, which is highly compatible with the judgment of the professional teacher and more accurately identifies the training demand of the majority of trainees for the knowledge point C. The model provides a more scientific and reasonable curriculum and also provides a better classification of knowledge points. The model provides a more scientific and reasonable course design idea. Under the gradual course arrangement, the mastery of the knowledge points of the 20 trainees all shows a steady improvement trend. From the highest score of no more than 17 points in the first week to the 16th week at the end of the semester, all the trainees scored more than 70 points, which maximized the training efficiency. This study develops a reasonable course design based on the relationship between knowledge points, which can ensure gradual and interrelated progress and improve training efficiency and quality.

Design of a Hybrid Movie Recommender System Using Machine Learning

The primary aim of recommender system is to predict items which are of most interest to the users and today recommender systems play a vital role in boosting the sales in any e-commerce based platform. The present paper proposes an approach for recommending movies to the users on the basis on their choices. A novel technique for evaluation of collaborative filtering using SVD and hit ratio as a metric is taken in our proposed approach. We attempted to build a model-based Collaborative filtering technique. The proposed paper makes use of matrix factorization techniques like SVD & SVD++ for filtering movie recommendation system based on latent features. It makes better recommendations based on choice of user because it captures the underlying features driving the raw data. In this paper we are proposing a hybrid recommender system fusion of Content Based and SVD to get a new hybrid recommender system. Our proposed model gives the value of RMSE 0.87 for SVD model and RMSE 0.938 for SVD++ model. Keywords-- Collaborative filtering, movie recommendation, SVD, content based filtering

Recommendation method for fusion of knowledge graph convolutional network

In the application of internet of vehicles system, it is particularly important to obtain real-time and effective vehicle information and provide personalized functional services for vehicle operation. This algorithm combines knowledge graph technology with convolutional network and presents a new algorithm model, that is, when calculating the representation of a given entity in the knowledge graph, the information of the neighboring entity is combined with the deviation. Through the integration of neighbor entity information, the local neighborhood structure can be better captured and stored in each entity, and the weight of different neighbor entities depends on the relationship between them and the specific user, which can better reflect the user's personalized interests, in order to fully demonstrate the characteristics of the entity. Compared with the traditional coordinated filtering technology SVD model, this model has improved accuracy and F1 value.

Cross Tensor Approximation Methods for Compression and Dimensionality Reduction

Cross Tensor Approximation (CTA) is a generalization of Cross/skeleton matrix and CUR Matrix Approximation (CMA) and is a suitable tool for fast low-rank tensor approximation. It facilitates interpreting the underlying data tensors and decomposing/compressing tensors so that their structures, such as nonnegativity, smoothness, or sparsity, can be potentially preserved. This paper reviews and extends state-of-the-art deterministic and randomized algorithms for CTA with intuitive graphical illustrations. We discuss several possible generalizations of the CMA to tensors, including CTAs: based on fiber selection, slice-tube selection, and lateral-horizontal slice selection. The main focus is on the CTA algorithms using Tucker and tubal SVD (t-SVD) models while we provide references to other decompositions such as Tensor Train (TT), Hierarchical Tucker (HT), and Canonical Polyadic (CP) decompositions. We evaluate the performance of the CTA algorithms by extensive computer simulations to compress color and medical images and compare their performance.

Laguerre Expansion Series Based Reduced Order Interval Systems

Model order reduction techniques have attracted researchers for the analysis of higher-order systems. The brief presents the order reduction of the interval system in state-space form using the Laguerre polynomial method with singular value decomposition. Laguerre - SVD model reduction technique is approved for the algorithms reduced computational complexity and stability perseverance. The proposed algorithm applies Laguerre approximation to interval system to compute an interval Krylov matrix using the Interval laboratory MATLAB toolbox. Then singular value decomposition is performed on the concatenated matrix with the upper limit and lower limit of the obtained interval Krylov matrix. The resultant column orthogonal matrix yields the reduced interval system through congruence transformation. The technique preserves the original system properties and is proved to be stable. Pertinent numerical results validate the proposed technique with examples from literature.

AnisoMPM

Dynamic fracture surrounds us in our day-to-day lives, but animating this phenomenon is notoriously difficult and only further complicated by anisotropic materials---those with underlying structures that dictate preferred fracture directions. Thus, we present AnisoMPM: a robust and general approach for animating the dynamic fracture of isotropic, transversely isotropic, and orthotropic materials. AnisoMPM has three core components: a technique for anisotropic damage evolution, methods for anisotropic elastic response, and a coupling approach. For anisotropic damage, we adopt a non-local continuum damage mechanics (CDM) geometric approach to crack modeling and augment this with structural tensors to encode material anisotropy. Furthermore, we discretize our damage evolution with explicit and implicit integration, giving a high degree of computational efficiency and flexibility. We also utilize a QR-decomposition based anisotropic constitutive model that is inversion safe, more efficient than SVD models, easy to implement, robust to extreme deformations, and that captures all aforementioned modes of anisotropy. Our elasto-damage coupling is enforced through an additive decomposition of our hyperelasticity into a tensile and compressive component in which damage is used to degrade the tensile contribution to allow for material separation. For extremely stiff fibered materials, we further introduce a novel Galerkin weak form discretization that enables embedded directional inextensibility. We present this as a hard-constrained grid velocity solve that poses an alternative to our anisotropic elasticity that is locking-free and can model very stiff materials.

A dog food recommendation system based on nutrient suitability

AbstractThe demand for a food recommendation service for dogs has rapidly increased with the increasing number of pet owners, because it is generally difficult for dog owners to find food that is perfectly suitable for their dogs' health condition. The purpose of this study is to develop an algorithm for recommending dog food that contains appropriate nutrients based on the physical and health conditions of the dogs. This study proposes a nutrient profiling‐based recommendation algorithm (NRA) for dog food. The proposed algorithm tries to recommend appropriate or inappropriate dog food by using collective intelligence based on user experience and the prior knowledge of experts. Based on the physical and health status of dogs, this study extracts which nutrients are necessary for the dogs and recommends the most suitable dog food containing these nutrients. A performance evaluation was implemented in terms of recall, precision, F1 and AUC. As a result of the performance evaluation, the AUC performance of this NRA is 20% higher than k‐NN and 9.7% higher than the SVD model. In addition, the NRA proved to be an evolving system in which the performance of recommendations improves as users' feedback accumulates.

A Study of the Drivers of Movie Rating Recommendations: Optimal Movie Prediction via Linear Regression

In recent years, online retailers have increasingly relied on state of the art algorithms to help predict user satisfaction and recommend movies to consumers. The major limitation of advanced state of the art computational algorithms such as SVD and latent factor models using implicit feedback is that these conceal the marginal effects of the most significant independent variables. This paper uses traditional linear regression to develop a recommender system which is as good as more advanced computational algorithms. Results reveal that the most important variables in predicting movie recommendations are ratings of user with extreme differences on other movies, age specific (conditioned) effects of movie ratings per movie and user average ratings conditioned on genres. These 3 elements allow regression models to match black box and matrix algorithms in recommender movie rating performance. This furthers the science behind recommender and also discusses a shock expectation shock minimization theory for enhancing movie watching experiences. Priming the users with appropriate expectations or frames for watching a movie should enhance user satisfaction. This supports query theory which states preferences are not recalled but dynamically built and can be influenced.

Hybrid Svd Model For Document Representation

Document clusters are the way to segment a certain set of text into racial groups. Nowadays all records are in electronic form due to the problem of retrieving appropriate document from the big database. The objective is to convert text consisting of daily language into a structured database format. Different documents are thus summarized and presented in a uniform manner. Big quantity, high dimensionality and complicated semantics are the difficult issue of document clustering. The aim of this article is primarily to cluster multi-sense word embedding using three distinct algorithms (K-means, DBSCAN, CURE) using singular value decomposition. In this performance measures are measured using different metrics.

Robust weighted SVD-type latent factor models for rating prediction

Recommending system is a popular tool in many commercial or social platforms which finds interesting products for users based on their preference history. Predicting the ratings of items, such as movies, plays an essential role in the recommending system. In this context, we develop a new type of latent factor models by attaching weights to the entries of the incomplete ratings matrix. The weights are computed after estimating the user/item mean errors caused by the basic SVD model under the low-rank assumption on the ratings matrix. To accelerate the optimization process of our proposed models and other existing SVD-type models, a special design of the initial guess is suggested. In the experiments on real-world datasets, the proposed weighted models outperform other SVD-type methods, and the usage of the special initial guess improves the optimization significantly, obtaining lower MRSEs within fixed number of iterations, in comparison with the random initial guess. Furthermore, artificially noised datasets are taken to evaluate the methods, where the weighted models still perform better than other SVD-type models, implying their effectiveness and robustness in noised environment.

Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data

In this article, we consider the sparse tensor singular value decomposition, which aims for dimension reduction on high-dimensional high-order data with certain sparsity structure. A method named sparse tensor alternating thresholding for singular value decomposition (STAT-SVD) is proposed. The proposed procedure features a novel double projection & thresholding scheme, which provides a sharp criterion for thresholding in each iteration. Compared with regular tensor SVD model, STAT-SVD permits more robust estimation under weaker assumptions. Both the upper and lower bounds for estimation accuracy are developed. The proposed procedure is shown to be minimax rate-optimal in a general class of situations. Simulation studies show that STAT-SVD performs well under a variety of configurations. We also illustrate the merits of the proposed procedure on a longitudinal tensor dataset on European country mortality rates. Supplementary materials for this article are available online.

Abstract 2079: Osimertinib, an irreversible mutant selective EGFR tyrosine kinase inhibitor, exerts anti tumor activity in NSCLC harbouring exon 20 insertion mutant-EGFR

Abstract EGFR exon 20 insertion (Ex20Ins) mutations represent a combination of in-frame insertions and/or duplications that account for 4-10% of all EGFR mutants in non-small cell lung cancer (NSCLC). To date, more than one hundred different Ex20Ins mutations have been identified. With the notable exception of the rare A763_Y764insFQEA insertion (<1%), EGFR Ex20Ins mutations are clinically unresponsive to early generation EGFR inhibitors, the standard of care for NSCLC patients with EGFR Ex20Ins mutations is chemotherapy. Therefore, a significant unmet need remains requiring the development of an EGFR TKI agent that can more effectively target NSCLC with EGFR Ex20Ins mutations. Osimertinib is a newly developed EGFR TKI approved for the treatment of advanced NSCLC with EGFR T790M tumors. This work describes the potential of osimertinib and AZ5104, a circulating metabolite of osimertinib, in Ex20Ins tumours. Using CRISPR-Cas 9 genome editing technology, we engineered cellular disease relevant models carrying the most prevalent Ex20Ins mutations, namely Ex20Ins D770_N771InsSVD (22%) or Ex20Ins V769_D770InsASV (17%). Using these models, we show that osimertinib and AZ5104 inhibit signalling pathways and cellular growth of Ex20Ins CRISPR-Cas9 engineered cell lines in vitro. This translates into sustained tumor growth inhibition in vivo in both the Ex20Ins SVD (65%, p<0.001 & 95%, p<0.001 respectively at day 14) and Ex20Ins ASV (82%, p<0.001 & 95%, p<0.001 respectively at day 14) xenograft models when compared to the control group. Importantly in vivo osimertinib was dosed at exposures consistent with the 80 mg osimertinib clinical dose. Moreover, a dual EGFR blockade strategy combining osimertinib or AZ5104 with cetuximab (an EGFR antibody) improved the anti tumor effect further. We also describe the anti-tumor activity of osimertinib and AZ5104 using a series of patient derived xenograft models harbouring the rarer Ex20Ins H773-V774insNPH and M766-A767insASV mutations. In addition, we performed pharmacodynamic studies to explore the relationship between efficacy and target/pathway modulation. These studies establish a clear relationship between depth and duration of phospho-EGFR inhibition and anti tumor efficacy. Interestingly, we observed that downstream signalling molecules displayed a more transient inhibition than the phospho EGFR signal. Two patients from the AURA Phase 2 osimertinib trials with plasma positive Ex20Ins (concurrent with Ex19del and T790M) had durable PFS responses of 6.4 and 13.9 months, supporting the premise that osimertinib has the potential to be clinically beneficial in tumors harboring Ex20Ins. The work presented herein demonstrates that osimertinib has the potential to improve upon the current treatment options for NSCLC patients whose tumors harbour an Ex20Ins mutation, and warrants its further clinical investigation. Citation Format: Nicolas Floc'h, Susan Ashton, Ambra Bianco, Nicola Colclough, Darren AE Cross, Maria Emanuela Cuomo, M. Raymond V. Finlay, Matthew J Martin, Ludovic Menard, Darren McKerrecher, Daniel J O'Neill, Jonathan P Orme, Anna D Staniszewska, Richard A Ward, James W T Yates. Osimertinib, an irreversible mutant selective EGFR tyrosine kinase inhibitor, exerts anti tumor activity in NSCLC harbouring exon 20 insertion mutant-EGFR [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2079. doi:10.1158/1538-7445.AM2017-2079

Matrix Factorization With Rating Completion: An Enhanced SVD Model for Collaborative Filtering Recommender Systems

Collaborative filtering algorithms, such as matrix factorization techniques, are recently gaining momentum due to their promising performance on recommender systems. However, most collaborative filtering algorithms suffer from data sparsity. Active learning algorithms are effective in reducing the sparsity problem for recommender systems by requesting users to give ratings to some items when they enter the systems. In this paper, a new matrix factorization model, called Enhanced SVD ( ESVD ) is proposed, which incorporates the classic matrix factorization algorithms with ratings completion inspired by active learning. In addition, the connection between the prediction accuracy and the density of matrix is built to further explore its potentials. We also propose the Multi-layer ESVD , which learns the model iteratively to further improve the prediction accuracy. To handle the imbalanced data sets that contain far more users than items or more items than users, the Item-wise ESVD and User-wise ESVD are presented, respectively. The proposed methods are evaluated on the famous Netflix and Movielens data sets. Experimental results validate their effectiveness in terms of both accuracy and efficiency when compared with traditional matrix factorization methods and active learning methods.

An Enhanced Approach for Face Recognition of Newborns using HMM and SVD Coefficients

ABSTRACT The problems of newborn’s abduction, mixing, swapping, etc. are increasing day-by-day. This problem has now reached a global aspect, as the consequences have now become critical. The researches performed to meet the challenge are very few. The growing problem motivated a need of sustainable system that can assist the hospital authorities and even the parents to keep a track of any newborn baby and his parents. The concept of biometric recognition has always proven to be a powerful tool when the identification of an individual comes into play. Therefore the face recognition among newborn is implemented in the proposed system. In order to deploy face recognition system for newborns, first a database is generated maintaining the images of a newborn and all the suspected parents. Matlab is used as a programming tool for the proposed work. The system trains the sample images of the parents using the HMM with a combination of SVD coefficients. The HMM and SVD models provided an approach to model a system that can develop a training sample of the image and can detect the image while any test sample is presented to the program. As a pre-processing method two-dimensional order static filtering is applied to the test images that improve the computational speed and accuracy of the system. Quantization SVD differentiates each image into a sequence of block and then each block of image is regarded as a numeric string. The HMM can effortlessly model the numeric block for training and recognition purpose. Matlab program generates a GUI that can train and recognize the image of a baby’s parent. The proposed system is user friendly and very quick in generating results. A fast and efficient system had been developed for the purpose of face recognition among the newborns

Multi-scale standardized spectral mixture models

Linear spectral mixture models can be standardized by using endmembers that span the global mixing space. By combining the benefits of location-specific mixture models with standardized spectral indices, standardized mixture models offer consistency, simplicity, inclusivity and applicability. We construct a globally representative mixing space using a spectrally diverse collection of 100 Landsat ETM+(Thematic Mapper & Enhanced Thematic Mapper+) subscenes. Global composites of 100,000,000 Landsat spectra, constructed from both exoatmospheric reflectance and atmospherically corrected surface reflectance, represent the spectral diversity of a wide range of terrestrial environments. Principal Component (PC) Analysis of the global composite shows that 99% of the spectral variance can be represented in a 3-dimensional mixing space of the low order PCs. Within this 3D space 98% of spectra are contained within a tetrahedral hull bounded by a continuous plane of substrates, and well-defined apexes corresponding to vegetation and dark endmembers. Suites of individual substrate, vegetation and dark endmember spectra are used to derive mean endmembers and to quantify the effects of endmember variability on fractions estimated from a standardized Substrate, Vegetation, and Dark (SVD) linear mixture model. Maximum endmember variability introduces less than 0.05 difference in S, V, and D fractions for most SVD models constructed from individual pixel endmember spectra giving less than 0.05 model misfit for more than 97% of pixels in the global composite. The mean SVD endmembers define a standard global mixture model for Landsat spectra. These SVD endmembers can be used to model mixed reflectance spectra from other sensors with similar spectral responses to Landsat ETM+. Comparisons of endmember fractions estimated from coincident acquisitions of Landsat TM and ETM+and WorldView-2 imagery show strong linear scaling for vegetation and dark fractions. Substrate fractions do not scale as linearly for the urban validation sites because the Landsat substrate endmember does not accurately represent the impervious surfaces imaged by WorldView-2. Comparisons of Landsat and WorldView-2 unmixed with the same Visible-Near Infrared (VNIR) endmembers derived from the global Landsat endmembers are also strongly correlated but with reduced bias. This linear scaling suggests that the Landsat global endmembers may provide a basis for standardized mixture models for WorldView-2 and other broadband sensors with spectral response similar to Landsat TM and ETM+. Comparisons of vegetation fractions with vegetation indices for the global composite show strong linear correspondence for Tasseled Cap Greenness and Enhanced Vegetation Index, with some degree of saturation at high fractions for the Soil Adjusted Vegetation Index and a wide range of responses for the Normalized Difference Vegetation Index.

Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis

Both moderate and high spatial resolution imagery can be used to quantify abundance and distribution of urban vegetation for urban landscape management and to provide inputs to physical process models. Estimation of vegetation fraction from Landsat ETM+ and Quickbird allows for operational monitoring and reconnaissance at moderate resolution with calibration and vicarious validation at higher resolution. Establishing a linear correspondence between ETM-derived vegetation fraction and Quickbird-derived vegetation fraction facilitates the validation task by extending the spatial scale from 30 × 30 m to a more manageable 2.8 × 2.8 m. A comparative analysis indicates that urban reflectance can be accurately represented with a three component linear mixture model for both Landsat ETM+ and Quickbird imagery in the New York metro area. The strong linearity of the Substrate Vegetation Dark surface (SVD) mixture model provides consistent estimates of illuminated vegetation fraction that can be used to constrain physical process models that require biophysical inputs related to vegetation abundance. When Quickbird-derived 2.8 m estimates of vegetation fraction are integrated to 30 m scales and coregistered to Landsat-derived 30 m estimates, median estimates agree with the integrated fractions to within 5% for fractions > 0.2. The resulting Quickbird-ETM+ scatter distribution cannot be explained with estimate error alone but is consistent with a 3% to 6% estimation error combined with a 17 m subpixel registration ambiguity. The 3D endmember fraction space obtained from ETM+ imagery forms a ternary distribution of reflectance properties corresponding to distinct biophysical surface types. The SVD model is a reflectance analog to Ridd's V–I–S land cover model but acknowledges the fact that permeable and impermeable surfaces cannot generally be distinguished on the basis of broadband reflectance alone. We therefore propose that vegetation fraction be used as a proxy for permeable surface distribution to avoid the common erroneous assumption that all nonvegetated surfaces along the gray axis are completely impermeable. Comparison of mean vegetation fractions to street tree counts in New York City shows a consistent relationship between minimum fraction and tree count. However, moderate and high resolution areal estimates of vegetation fraction provide complementary information because they image all illuminated vegetation, including that not counted by the in situ street tree inventory.