Earticle

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.

추천 시스템은 컨텐츠, 온라인 커머스, 소셜 네트워크, 광고 시스템 등 많은 분야에서 사용자가 관심 있을 만한 정보를 선별 제안함을 목적으로 활발하게 연구되고 있다. 그러나 과거 선호도 데이터를 기반으로 제안하는 추천시스템이 많고 과거 데이터가 적거나 없는 사용자를 대상으로는 제공하기 어려우므로 낮은 성능을 보인다는 부문에서 문제점이 있다. 따라서 더욱 고차원적인 데이터 분석에 관한 관심이 증가하고 있고 Matrix Factorization이 주목받고 있다. 이 논문은 그 중 추천시스템에서 주목받는 Factorization Machines Learning(FM)모델과 고차원 데이터 분석인 High-order Factorization Machines Learning(HOFM)의 비교와 재연을 연구하고 제안 한다.

The recommendation system is actively researched for the purpose of suggesting information that users may be interested in in many fields such as contents, online commerce, social network, advertisement system, and the like. However, there are many recommendation systems that propose based on past preference data, and it is difficult to provide users with little or no data in the past. Therefore, interest in higher-order data analysis is increasing and Matrix Factorization is attracting attention. In this paper, we study and propose a comparison and replay of the Factorization Machines Leaning(FM) model which is attracting attention in the recommendation system and High-Order Factorization Machines Learning(HOFM) which is a high - dimensional data analysis.

A classic document clustering technique may incorrectly classify documents into different clusters when documents that should belong to the same cluster do not have any shared terms. Recently, to overcome this problem, internal and external knowledge-based approaches have been used for text document clustering. However, the clustering results of these approaches are influenced by the inherent structure and the topical composition of the documents. Further, the organization of knowledge into an ontology is expensive. In this paper, we propose a new enhanced text document clustering method using non-negative matrix factorization (NMF) and WordNet. The semantic terms extracted as cluster labels by NMF can represent the inherent structure of a document cluster well. The proposed method can also improve the quality of document clustering that uses cluster labels and term weights based on term mutual information of WordNet. The experimental results demonstrate that the proposed method achieves better performance than the other text clustering methods.

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.

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.

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.

Background: With volatile organic compounds (VOCs) containing aromatic and halogenated hydrocarbons such as benzene, toluene, and xylene that can adversely affect the respiratory and cardiovascular systems when a certain concentration is reached, it is important to accurately evaluate the source and the corresponding health risk effects. Objectives: The purpose of this study is to provide scientific evidence for the city of Seoul's VOC reduction measures by confirming the risk of each VOC emission source. Methods: In 2020, 56 VOCs were measured and analyzed at one-hour intervals using an online flame ionization detector system (GC-FID) at two measuring stations in Seoul (Gangseo: GS, Bukhansan: BHS). The dominant emission source was identified using the Positive Matrix Factorization (PMF) model, and health risk assessment was performed on the main components of VOCs related to the emission source. Results: Gasoline vapor and vehicle combustion gas are the main sources of emissions in GS, a residential area in the city center, and the main sources are solvent usage and aged VOCs in BHS, a greenbelt area. The risk index ranged from 0.01 to 0.02, which is lower than the standard of 1 for both GS and BHS, and was an acceptable level of 5.71×10^-7 to 2.58×10^-6 for carcinogenic risk. Conclusions: In order to reduce the level of carcinogenic risk to an acceptable safe level, it is necessary to improve and reduce the emission sources of vehicle combustion and solvent usage, and eco-car policies are judged to contribute to the reduction of combustion gas as well as providing a response to climate change.

최근, 소셜 네트워크 서비스에서 딥러닝을 활용한 추천시스템이 활발하게 연구되고 있다. 하지만 딥러닝을 이용 한 추천시스템의 경우 콜드스타트 문제와 복잡한 연산으로 인해 늘어난 학습시간이 단점으로 존재한다. 본 논문에서는 사용자의 메타데이터를 활용하여 사용자 맞춤형 운동 루틴 추천 알고리즘을 제안한다. 본 논문에서 제안하는 알고리즘은 메타데이터(사용자의 키, 몸무게, 성, 등)를 입력받아 설계된 모델에 적용한다. 본 논문에서 제안한 운동 추천시스템 모델 은 matrix factorization 알고리즘과 multi-layer perceptron을 활용한 neural collaborative filtering(NCF) 알고 리즘을 기반으로 설계된다. 제안된 모델은 사용자 메타데이터와 운동 정보를 입력받아 학습을 진행한다. 학습이 완료된 모델은 특정 운동이 입력되면 사용자에게 추천도를 제공한다. 실험 결과에서 제안하는 운동 추천시스템 모델이 기존 NCF 모델보다 10% 추천 성능 향상과 50% 학습 시간 단축을 보였다.

Recently, a recommendation system using deep learning in social network services has been actively studied. However, in the case of a recommendation system using deep learning, the cold start problem and the increased learning time due to the complex computation exist as the disadvantage. In this paper, the user-tailored exercise routine recommendation algorithm is proposed using the user's metadata. Metadata (the user's height, weight, sex, etc.) set as the input of the model is applied to the designed model in the proposed algorithms. The exercise recommendation system model proposed in this paper is designed based on the neural collaborative filtering (NCF) algorithm using multi-layer perceptron and matrix factorization algorithm. The learning proceeds with proposed model by receiving user metadata and exercise information. The model where learning is completed provides recommendation score to the user when a specific exercise is set as the input of the model. As a result of the experiment, the proposed exercise recommendation system model showed 10% improvement in recommended performance and 50% reduction in learning time compared to the existing NCF model.

The metadata information of users and items for enhancing the recommendation system robustness has important valuable. Following this design philosophy, this paper first presents the user suspects assessment strategy based on Probabilistic Latent Semantic Analysis, the user suspected sexual and generic items such as meta-information to model parameters and Logistic Regression way into Bayesian probabilistic matrix factorization (BPMF) model, and then proposes Metadata-enhanced Variational Bayesian Matrix Factorization (MVBMF), designed a model of incremental learning strategy based on robust linear regression, in order to reduce the demand for model rebuilding. Experimental results show that MVBMF can effectively defend against shilling attacks and also has a high level of performance for strong and weak generalization.

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.

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.

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.