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The rapid growth of e-commerce platforms demands recommendation systems that balance scalability with representational expressiveness. While Matrix Factorization (MF)-based collaborative filtering offers computational efficiency, it fails to capture high-order contextual relationships among items. Graph Neural Networks (GNNs) address this limitation but incur prohibitive computational costs in large-scale, real-time settings. To resolve this tradeoff, we propose a unified hybrid framework integrating MFbased candidate generation and GNN-based re-ranking within a retrieve–then–rank architecture. The MF stage efficiently generates a candidate set, while the GNN stage refines rankings via a heterogeneous graph incorporating user–item interactions, item attributes, and category structures. We further introduce an availability-aware scoring mechanism that integrates real-time stock information to enhance practical applicability. Extensive experiments on a large-scale e-commerce dataset demonstrate that the proposed framework consistently outperforms baseline methods, effectively mitigates data sparsity, and maintains computational efficiency suitable for real-world deployment.

This paper suggests a variation of a well-known probabilistic matrix factorization algorithm which is commonly used in data analysis and scientific computing, and which has been considered recently to serve natural language processing. The proposed variation is meant to take benefit from the fact that matrices processed in natural language processing tasks are normally sparse rectangular matrices with one dimension much larger than the other, and this can be used to ensure adequate accuracy with acceptable computation time. Preliminary experiments on real-world textual corpora show that the proposed algorithm achieves relevant improvements compared to the original one.

본 연구에서는 초미세먼지(PM2.5)의 배출원 및 기여도를 파악하기 위하여 PMF 수용 모델을 활용하여 분석을 진행하 였다. 측정지점은 주변에 산단지역, 주거지역, 농촌지역이 위치하여 복합적인 영향을 받을 수 있는 대전지역으로 선정하 였다. 겨울(1월)과 여름(5, 6월)기간 동안 측정데이터를 수집하였고 분석하였다. PMF 모델 분석 결과 7가지 Factor 즉, 먼 지, 해염입자, 2차 생성 질산염 및 염산염, 2차 생성 황산염, 산업배출, 석탄연소, 자동차배출로 분류하였다. 계절별 기여 도를 확인한 결과 먼지와 해염입자의 경우 5-6월에 발생한 황사 이벤트로 인해 그 기간 동안 기여도가 높게 나타났다. 2 차 생성물질의 경우 질산염과 염산염은 상온에서 상변화가 이루어지는 특징으로 인해 여름보다 겨울에 더 높게 나타났 으며, 석탄연소의 경우 겨울에 더 높게 나타났다. 이를 통해 PMF 결과가 양호하게 나타난 것으로 판단하였다.

In this study, analysis was conducted using the PMF acceptance model to identify the emission source and contribution of fine particle (PM2.5). The measurement point was selected as the Daejeon area, where industrial complex areas, residential areas, and rural areas are located nearby, and thus can be affected by multiple factors. Measurement data were collected and analyzed during the winter (January) and summer (May, June) periods. As a result of the analysis of the PMF model, it was classified into 7 factors, namely, dust, sea salt particles, secondary nitrate and chloride, secondary sulfate, industrial emissions, coal combustion, and vehicle emissions. As a result of checking the contribution by season, the contribution of dust and sea salt particles was high during that period due to the yellow dust event that occurred in May-June. In the case of secondary products, nitrate and hydrochloride were higher in winter than in summer due to the phase change at room temperature, and in the case of coal combustion, they were higher in winter. Through this, it was judged that the PMF result was good.

Purpose: This study introduces a methodological framework for muscle synergy analysis to enhance the understanding of sports rehabilitation researchers regarding its practical applications. This work is intended to provide clinicians with a practical understanding of muscle synergy analysis and promote its effective application in rehabilitation. Methods: Muscle synergy refers to the concept of explaining muscle coordination during movements as a combination of a limited number of modules (synergies), thereby offering a powerful approach for quantifying muscle activation patterns. In this framework, electromyography (EMG) signals are decomposed using a nonnegative matrix factorization algorithm, and the optimal number of synergies is determined based on the “variance accounted for” criterion. Each synergy represents a distinct group of muscles, specifying their activation timing and relative contributions throughout movement. To facilitate comprehension, we present a case study employing simulated two-channel EMG data, assuming a single synergy. Results: The dataset included five different signal patterns reflecting proportional, inverse, and independent relationships, mimicking real-world EMG signals observed in sports and clinical settings. Conclusion: This work is intended to provide clinicians with a practical understanding of muscle synergy analysis and promote its effective application in rehabilitation.

Matrix factorization-based approaches have proven to be efficient for recommender systems. However, due to the time complexity in composing recommendations, matrix factorization-based approaches are inefficient in dealing with large scale datasets. In this paper, we present a new similar user index-based matrix factorization approach for large scale recommender systems. Finding similar users is the most time-consuming phase in large scale recommender systems. To reduce time to find the similar users, we propose a similar user index in matrix factorization. This paper describes the index structure and algorithms. Several experiments are performed. The results show that our approach is more efficient in dealing with the large dataset as compared with matrix factorization approach without the similar user index.

Human mobility estimation plays a key factor in a lot of promising applications including location-based recommendation systems, urban planning, and disease outbreak control. We study the human mobility estimation problem in the case where recent locations of a person-of-interest are unknown. Since matrix decomposition is used to perform latent semantic analysis of multi-dimensional data, we propose a human location estimation algorithm based on matrix factorization to reconstruct the human movement patterns through the use of information of persons with correlated movements. Specifically, the optimization problem which minimizes the difference between the reconstructed and actual movement data is first formulated. Then, the gradient descent algorithm is applied to adjust parameters which contribute to reconstructed mobility data. The experiment results show that the proposed framework can be used for the prediction of human location and achieves higher predictive accuracy than a baseline model.

Aiming at the problem that the traditional Collaborative Filtering algorithm has low recommendation accuracy, in the paper, we propose a collaborative filtering recommendation algorithm based on the global trust degree integrating the direct trust information in the social networks. We first transform the local trust relationships to the global trust relationship by the rules in the trust network, and get the trust rank of all users in the trust networks; Then we use the global trust value to instead of the similarity information value as the weights of a predicted formula in the traditional collaborative recommendation algorithm, and integrate the weights to the matrix factorization-based recommendation model.

Aiming at the problems that the existing model-based collaborative filtering algorithm has low recommendation accuracy and small recommendation coverage, we propose a collaborative filtering recommendation algorithm based on the trust propagation by introducing the trust information of social network to extend the matrix factorization-based recommendation model. We first design a set of trust propagation rules based on the direct trust relationships of the social network, so as to propagate the trust relationship in the social networks, and get to quantize the new trust relationship. Then we load the quantitative trust relations after the trust propagation as the trust weight into the matrix factorization-based model according to the characteristics that the matrix factorization technique can reduce the dimension of large-scale datasets.

Many methods have been applied in image compression and Non-negative matrix factorization (NMF) is one of some approach which could be applied in image compression. Non-Negative Matrix Factorization (NMF) was a realtively new approach to decompose data into two factors with non-negative entries. This paper shows that the NMF method can be applied in image compression using a model of 2x2 pixels weighted matrix rather than using the model of 200x200 pixels, 10x10 pixels and 4x4 pixels weighted matrix. This paper also shows that the 2x2 Weighted matrix model example of ((65536, 1), (1, 65536)) was the best weighted matrix for NMF image compression. Finally, this research proved that by using the weighted matrix with higher determinant value could gain smaller size compressed image.

As an important tool to help users filter Internet information, recommender system has played a very important role wherever in academia or in industrial area. During the past years, different recommendation approaches based on the social network have been proposed with the rapid development of online social networks. Different from the traditional ones which assume all the users are independent and identically distributed, these approaches follow the intuition that a person’s implicit or explicit social network will affect his behaviors on the Web. In this paper, on the basis of the existing work, we fuse a baseline predictor model with an improved social recommendation model and propose a social recommendation algorithm based on probability matrix factorization. The experimental result shows that our method outperforms the existing approaches in accuracy.

The matrix factorization algorithms such as the matrix factorization technique (MF), singular value decomposition (SVD) and the probability matrix factorization (PMF) and so on, are summarized and compared. Based on the above research work, a kind of improved probability matrix factorization algorithm called MPMF is proposed in this paper. MPMF determines the optimal value of dimension D of both the user feature vector and the item feature vector through experiments. The complexity of the algorithm scales linearly with the number of observations, which can be applied to massive data and has very good scalability. Experimental results show that MPMF can not only achieve higher recommendation accuracy, but also improve the efficiency of the algorithm in sparse and unbalanced data sets compared with other related algorithms.

With the increasing abundance of Web Services across Internet, Quality of Service (QoS)-based service recommendation has become a hot issue. It is necessary to predict the missing values of QoS for service recommendation. Because Web services run on the Internet, their network locations may be anther critical factor for QoS prediction. Although there have existed many works on QoS prediction, few consider the influence of the network locations of users or Web Services. In this paper, we propose a novel collaborative QoS prediction framework with network location-based regularization (NLBR). We first elaborate the popular Matrix Factorization (MF) model for missing values prediction. Then, by taking advantage of the local connectivity between Web services users, we incorporate network location information to identify the neighborhood. We conduct the experiments on a public large-scale real-world QoS dataset, Experiments show that our proposed approaches have the better prediction performance compared with the existed approaches.

In order to deal with the issue of network attacks and enhance the security of the network environment, intrusion detection is gaining more and more attention all over the world. In this paper, a novel intrusion detection method based on improved triangular matrix factorization is presented. As a type of famous mathematical tool, triangular matrix factorization has a good ability to reduce the large amount of high dimensional data. However, the traditional triangular matrix factorization has its inherent drawbacks such as the difficulty of setting the parameter adaptively, so the model of an improved version of triangular matrix factorization together with its concrete algorithm is proposed in this paper firstly. Then, improved triangular matrix factorization is employed to convert the high dimensional data of the network into low dimensional vectors of several matrices, with which the anomaly detection can be realized. Experimental results indicate that the proposed method is promising, and it does significantly enhance the detection accuracy and computational efficiency compared with other current popular ones.

Disease conditions in the human brain can be detected by using medical image analysis. Content Based Image Retrieval of medical images can be used as an alternative to recognize the medical images of the human brain. In CBIR, feature extraction and recognition methods was an important feature because of medical image was different from the general image. In this study medical images that contain clinical information will be used as feature extraction using a canny edge detection and recognition features using non-negative matrix Factorization (NNMF). The purpose of this paper was to describe the use of canny edge detection and NNMF in CBIR. So CBIR could provide information on brain diseases and abnormalities. The results showed that the detection of disease in the human brain can be done by using both methods with good results if done preprocessing using histogram equalization.

We propose a convolutive non-negative matrix factorization method to improve the intelligibility of speech signal in the context of adverse noise environment. The noise bases are prior learned with Non-negative Matrix Factorization (NMF) algorithm. A modified convolutive NMF with sparse constraint is then derived to extract speech bases from noisy speech. The divergence function is selected as an objective function to get a multiplicative update of speech base and its corresponding weight. The weights of prior learned noise bases are also updated in the update rule. Listening experiments are conducted to assess the intelligibility performance of speech synthesized using the proposed algorithm. Experimental results indicate that the proposed method is very effective to improve the intelligibility of the noisy speech in various noise contexts and it outperforms conventional algorithms.

Transmissibility function (TF) was used in structural state detection which examined the change of the response-only vibration characteristics. The technology of signal correlation was utilized to obtain the TF between two testing points. Multiple TFs under different states were calculated as basic TFs and formed a state matrix. Subsequently, non-negative matrix factorization (NMF) was performed for the state matrix. Then both the basic and testing TFs were projected to the feature subspace derived from NMF so as to obtain the state feature index vectors, respectively. Finally, the Euclidean distance between state feature index vectors was defined as the state indicator. The experimental results indicated that this method can achieve better detection accuracy than that using magnitude indicator. Actually, a result with 100 percent correct detection was achieved when proper rule of dimensionality reduction was selected. This method is essentially a multivariate statistical process monitoring (MSPM) method. It is feasible in vibration-based structural state detection in the situation where the excitation signals are unavailable or inaccessible.

This paper presents a new recognition algorithm for plant pathology images based on the Non-negative Matrix Factorization, the proposed algorithm is combined with optimal wavelet packet basis to recognize patterns and conduct data encoding in the internet of things oriented intelligent agricultural system. The experimental results show that the performance of the proposed recognition algorithm is far better than those of the principal component analysis and linear discriminant analysis, and the recognition rate are improved, on average, about 14.65% and 11.18% higher than the rates of the above algorithms respectively. The presented algorithm is characterized by the fast speed, high calculation accuracy and easy hardware implementation.

After the image decomposition with Non-subsampled Shearlet Transform (NSST), the transform coefficients have a larger redundancy. In order to reduce the redundant information, an image fusion algorithm based on NSST and non-negative matrix factorization (NMF) is introduced. The source images are decomposed with NSST into the low-frequency coefficients and the high-frequency sub-band coefficients. The low-frequency coefficients are fused based on NMF theory. The high-frequency coefficients are fused based on Regional Sum Modified-Laplacian (SML) Maximum. Finally, the inverse NSST is used to reconstruct the final fused image. The proposed algorithm can effectively remove redundant information, extract global features and capture more direction details information of multi-source image. Experiments show that the proposed algorithm has obvious advantages and the fused image quality has been greatly improved. The presented algorithm is superior to other fusion algorithms from the objective parameters.

마이크로어레이 (microarray) 기술이 개발된 후로 연관된 유전자 클러스터 (cluster)를 찾는 문제는 깊이 연구되어왔다. 이 문제는 핵심적인 과제 중 하나는 생물학적으로 타당한 클러스터의 수를 결정하는 데 있다. 본 논문은 최적의 클러스터 수를 결정하는 기준을 제시하고, non-negative factorization (NMF)를 이용해 클러스터 centroid의 패턴을 찾는 방법을 제안한다. NMF에 의해 발견된 각각의 패턴은 생물학적 프로세스의 특정 부분으로 해석될 수 있다. NMF는 factor matrix의 entity를 non-negative로 제약 (constraint)하고, 이 제약은 오직 additive combination만 허용하기 때문에 이러한 부분적인 패턴을 찾아낼 수 있다. NMF의 유용성은 이미지 분석과 텍스트 분석에서 이미 입증되어 있다. 본 논문에서 제안한 방법에 의해 위의패턴과 유사한 발현 패턴을 갖는 유전자를 모을 수 있었다. 제안된 방법은 human fibroblast데이터와 yeast cell cycle 데이터에 적용해 성능을 입증하였다.