Rating Matrix Research Articles

The Environmental Aspect & Impacts for Industrial Sites: Methodological Framework and Evaluation

Identifying linked environmental aspects and evaluating related impacts is a crucial requirement of any environmental management system for industrial facilities. Industrial sites should guarantee that a complete procedure for aspect identification and impact assessment is in place to fulfil international environmental protection and preservation requirements. The goal of this research was to develop a new procedure. Determine which environmental aspects of industrial sites (actions, products, and services) have or can have substantial consequences, and review and monitor their control regularly. Professional judgment will play an essential part in determining how to address significance in many cases, and this can be aided by collaboration with relevant stakeholders. The study's conclusion was creating a new environmental aspect evaluation system based on newly developed aspects and impacts rating matrix. Industrial sites could use this approach to identify and manage the environmental aspects of their operations.

Multi-order nearest neighbor prediction for recommendation systems

Recommendation systems (RSs) can establish a relationship between users and items and recommend the seemingly unrelated but actually interesting items to target users by utilizing their behavior. However, the recommendation performance of RSs is seriously affected by the issue of data sparsity because the information amount that each user involves is very limited with the continuous growth of both users and items. To solve the issue of data sparsity in RSs, a novel collaborative filtering prediction method is proposed, called multi-order nearest neighbor prediction (MNNP). The concept of multi-order nearest neighbors is an extension of friends of a friend. By using multi-order nearest neighbors, MNNP can not only expand the range of neighbors but also implement the neighbor propagation efficiently. For a target user, MNNP needs to search its multi-order nearest neighbors and successively uses them to iteratively update the rating matrix. To show the procedure of MNNP, we illustrate it by an example. Extensive experiments on real-world datasets show that MNNP has a good performance.

A 25-reader performance study for hepatic metastasis detection: lessons from unsupervised learning.

There is substantial variability in the performance of radiologist readers. We hypothesized that certain readers may have idiosyncratic weaknesses towards certain types of lesions, and unsupervised learning techniques might identify these patterns. After IRB approval, 25 radiologist readers (9 abdominal subspecialists and 16 non-specialists or trainees) read 40 portal phase liver CT exams, marking all metastases and providing a confidence rating on a scale of 1 to 100. We formed a matrix of reader confidence ratings, with rows corresponding to readers, and columns corresponding to metastases, and each matrix entry providing the confidence rating that a reader gave to the metastasis, with zero confidence used for lesions that were not marked. A clustergram was used to permute the rows and columns of this matrix to group similar readers and metastases together. This clustergram was manually interpreted. We found a cluster of lesions with atypical presentation that were missed by several readers, including subspecialists, and a separate cluster of small, subtle lesions where subspecialists were more confident of their diagnosis than trainees. These and other observations from unsupervised learning could inform targeted training and education of future radiologists.

Improved Collaborative Filtering Recommender System Based on Missing Values Imputation on E-Commerce

One of the important aspects in e-commerce is how to recommend a product to users accurately. To achieve this goal, many e-commerce starts to build and research about recommender system. Many methods can be used to build a recommender system, one of them is using the collaborative filtering technique. This technique often experiences data sparsity problem that can impact to the recommender system prediction accuracy. To solve this problem, we apply improved collaborative filtering. This method predicts the missing values in the user item rating matrix. First, we do an initial selection to determine potential users who have the same characteristics with the active user. After that, we calculate the average distance between the active user and the other selected user. Next, we calculate missing values prediction. Missing values predictions is only done for items that have never been rated by other’s selected user but has been rated by the active user. We used Amazon electronic product with high sparsity level in this research to simulate the actual condition of e-commerce. We used MAE and RMSE to measure prediction accuracy. The methods we apply succeeds to improve the prediction accuracy compare to the conventional collaborative filtering method. The average MAE for method that we apply is 0.78 and RMSE 1.07

A recommender system fused with implicit social information through network representation learning

Traditional recommender systems have always suffered from cold start and data sparsity problems by leveraging only a rating matrix. Social recommender systems try to address such problems by leveraging the user's social information, such as trust relationships, as auxiliary information. However, such explicit social information is not always available. In this paper, an implicit interest relationship mining approach based on network representation learning for recommendation, called INRec, is proposed to deeply mine implicit social information and fuse both explicit and implicit social information into a matrix factorization (MF) model and thus improve the effectiveness of the recommendations is proposed. Specifically, a modified one-mode projection method is first designed to construct an interest-connected user interaction network, and a network representation learning method is then employed to generate low-dimensional user representations. Abundant user interest relationships can be later obtained from user representations through a similarity calculation. Finally, a multi-source information fusion approach is applied to incorporate explicit and implicit social relationships into the MF model. The experimental results demonstrated that the average MAE and RMSE by INRec achieves 0.7415 and 0.9633 on three datasets (FilmTrust, CiaoDVD, Epinions), and it remarkably outperforms the state-of-the-art by a large margin.

Improving nonnegative matrix factorization with advanced graph regularization

Nonnegative Matrix Factorization (NMF) produces interpretable solutions for many applications including collaborative filtering. Typically, regularization is needed to address issues such as overfitting and interpretability, especially for collaborative filtering where the rating matrices are sparse. However, the existing regularizers are typically constructed from the factorization results instead of the rating matrices. Intuitively, we regard these existing regularizers as representing either user factors or item factors and anticipate that a more holistic regularizer could improve the effectiveness of NMF. To this end, we propose a graph regularizer based on a linear projection of the rating matrix, and call the resulting method: Linear Projection and Graph Regularized Nonnegative Matrix Factorization (LPGNMF). We develop two iterative methods to minimize the cost function and derive two update rules named LPGNMF and F-LPGNMF. Additionally, we prove the value of the objective function decreases with LPGNMF and converges to a fixed point with F-LPGNMF. Finally, we test these methods against a number of NMF algorithms on different data sets and show both LPGNMF and F-LPGNMF always achieve smaller errors based on two different error measures.

A new approach for the demonstration of acidizing parameters of carbonates: Experimental and field studies

Acid stimulation of carbonates is the most common method of production intensification in practice of hydrocarbon field development. Injection of acid compositions improves hydrodynamic connection of the wellbore with the reservoir by creating extended filtration channels – wormholes. Wormholes increase the effective drainage radius of production wells, which provides incremental oil. Scientists around the world have conducted a considerable amount of experimental and numerical research at the fundamental level aimed at studying the complex process of interaction of acid compositions with carbonate rock. The results of the studies repeatedly noted the critical role of technological parameters of acid treatment, the competent choice of which often provides a positive result of stimulation. However, a significant number of studies have been performed and tested only at the theoretical level or under ideal laboratory conditions, and their transfer to real heterogeneous productive reservoirs turns out to be a failure. Our study demonstrated a fundamentally new approach to justify the technological parameters of acid stimulation of carbonates (volume of acid and reaction time) based on laboratory experiments and field studies. We developed a rating matrix that characterized the effectiveness of acid treatments in terms of changes in well performance and reservoir properties, as well as the multiplicity of exposure, for a wide range of technological parameter changes. The application of this approach allows not only to evaluate the effectiveness of actually performed acid treatments, but also to quickly determine the perspective of different design options for future interventions. The operability of matrix was confirmed by positive pilot tests at wells in Perm Krai (Russia), and the test results were 8–31% higher than the planned results. The algorithm we described was useful in designing acid treatments. This method effectively improved the oil and gas recovery under the condition that the reserve structure deteriorated annually and the number of candidate wells decreased.

Hy-MOM: Hybrid Recommender System Framework Using Memory-Based and Model-Based Collaborative Filtering Framework

Abstract Lack of personalization, rating sparsity, and cold start are commonly seen in e-Learning based recommender systems. The proposed work here suggests a personalized fused recommendation framework for e-Learning. The framework consists of a two-fold approach to generate recommendations. Firstly, it attempts to find the neighbourhood of similar learners based on certain learner characteristics by applying a user-based collaborative filtering approach. Secondly, it generates a matrix of ratings given by the learners. The outcome of the first stage is merged with the second stage to generate recommendations for the learner. Learner characteristics, namely knowledge level, learning style, and learner preference, have been considered to bring in the personalization factor on the recommendations. As the stochastic gradient approach predicts the learner-course rating matrix, it helps overcome the rating sparsity and cold-start issues. The fused model is compared with traditional stand-alone methods and shows performance improvement.

Satisfaction with remote teaching in Thai higher education

Introduction. Emergency remote teaching (ERT) is meant to be a temporary shift from the normal modes of contact teaching. Such transition was imposed during the global pandemic in the spring of 2020, and higher education was required to shift entire curricula online in an attempt to curb the spread of the virus while maintaining continuity of its services. The disruptive overnight change and conversion of entire courses to ERT caused concerns, not only to the educators but also to the students who had little time to adapt to the new circumstances.Aim. The aim of the study was to examine student perceptions with regard to remote teaching during the global pandemic COVID-19. Moreover, the study aimed to identify attributes, which students deem as the most important during emergency remote teaching.Methodology and research methods. This mixed-method case study expands earlier research addressing those concerns, and adds to the body of knowledge by investigating how ERT is currently – during the second year of the pandemic – perceived by undergraduate students in Northeastern Thailand. Responses from a self-administered survey were collected and analysed (n = 363). Based on descriptive analysis, it was decided to conduct 12 unstructured interviews to investigate particular findings more thoroughly. An importance-performance rating matrix was used to determine the perceived satisfaction by the undergraduate students.Results and scientific novelty. The study identified that the students largely view ERT as inferior compared to traditional classroom teaching. Students claimed both lack of social interactions with peers and inability to seek academic support as the primary reasons. This study informs educators about student perceptions and preferences during these extraordinary circumstances of uncertain duration.Practical significance. The current research presents the recommendations that aim to provide institutions and educators with practical guidance on how to tackle the outlined issues.

Combining review-based collaborative filtering and matrix factorization: A solution to rating's sparsity problem

An important factor affecting the performance of collaborative filtering for recommendation systems is the sparsity of the rating matrix caused by insufficient rating data. Improving the recommendation model and introducing side information are two main research approaches to address the problem. We combine these two approaches and propose the Review-Based Matrix Factorization method in this paper. The method consists of two phases. The first phase is review-based collaborative filtering, where an item-topic rating matrix is constructed by the feature-level opinion mining of online review text. This rating matrix is used to derive item similarities, which can be used to infer unknown users' ratings of the items. The second phase consists of rating imputation, where we first fill some of the empty elements of the user-item rating matrix, then conduct matrix factorization to learn the latent user and item factors to generate recommendations. Experiments on two actual datasets show that our method improves the accuracy of recommendation compared with similar algorithms.

Hybrid Collaborative Filtering Algorithm Based on Sparse Rating Matrix and User Preference

This study presents a hybrid collaborative filtering recommendation algorithm for sparse data (HCFDS) to increase the recommendation impact by addressing the problem of data sparsity in standard collaborative filtering methods. To begin, the similarity calculation divergence is evident in a data sparse environment due to the difference in user scoring standards and the rise in weight of the same score in the overall score. The user similarity algorithm IU-CS and item similarity algorithm II-CS are suggested in this work by incorporating the score difference threshold and the same score penalty factor, in order to address the deviation of similarity computation caused by the excessive dilation. Second, this work offers a filling optimization technique for score prediction to address the issue of missing score matrix data. The II-CS algorithm presented in this work is used to forecast the missing items in the scoring matrix first, and then, the user’s preference score in the item category dimension is utilized to correct the score prediction value and fill the matrix. Finally, the IU-CS method presented in this work is used in this study to provide recommendations on the filled score matrix. Experiments indicate that, when compared to the preoptimization method and other algorithms, the optimized algorithm successfully solves the problem of data sparsity and the recommendation accuracy is considerably increased.

A cloud service recommendation method based on extended multi‐source information fusion

AbstractWith the rapid development of information technology, the problem of “information overload” emerges when users choose cloud services. How to integrate multi‐source information to achieve accurate service recommendation is an urgent problem to be solved by current recommendation systems. This article proposes a cloud service recommendation method based on extended multi‐source information fusion. First, we propose a score prediction based on matrix decomposition and topic matrix, and we fully mine existing explicit data and feedback data, such as user ratings, social trust information, reviews, and user personalized preferences and so on. Second, in order to solve the problems of data sparseness and cold start of the system, we integrate the score, social trust information and review into a comprehensive model through collaborative filtering (CF), and propose a multi‐source information fusion recommendation method. The CF fusion method mainly combines two parts: social matrix decomposition and topic matrix decomposition. Finally, in order to further improve the accuracy and scalability, the implicit feature matrix is integrated into the user rating matrix, and the original CF enhancement based on scoring matrix decomposition is a matrix decomposition method that can learn implicit features. Experimental results show that compared with other recommendation algorithms, the cloud service recommendation method proposed in this article can improve the recommendation accuracy and allow users to choose satisfactory cloud services.

Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach

Customers post online product reviews based on their own experience. They may share their thoughts and comments on items on online shopping websites. The sentiment analysis comprises of opinion or idea process and process of sorting high rating reviews according to how well the product satisfies. Opinion mining is a technique for extracting useful data from large amounts of texts in order to use those to enhance or expand a company's operations. According to consumer evaluations, many of the goods aren't as good as they seem. It's common that buyers submit their thoughts on a product but then forget to rate it. The prior data preprocessing is more efficient to extract the features by CNN approach. This proposed methodology breaks down each user's rating prediction model into two parts: one based on the review text and other based on the user rating matrix with the help of CNN feature engineering. The goal of this study is to classify all reviews into ratings by SVM model. This proposed classification model provides good accuracy to predict the online reviews efficiently. For reviews without ratings, a further prediction of feelings is generated using multiple classifiers. The benefits of this proposed model are honed using helpfulness ratings from a small number of evaluations such as accuracy, F1 score, sensitivity, and precision. According to studies using the standard benchmark dataset, the accuracy of customized recommendation services, user happiness, and corporate trust may all be enhanced by including review helpfulness information in the recommender system.

New Strategy Based on RBF Network to Develop a Collaborative Filtering Recommender System

Collaborative filtering is a popular recommendation algorithm. It predicts user's interests according to the ratings or behaviour of other users in the system. However, the collaborative filtering recommender system suffers from several major limitations including scalability, sparsity, and cold start. In this paper, a collaborative filtering recommendation approach using radial basis function (RBF) network and power method is proposed. The proposed system has offline and online phases. In the offline phase, the sparse user-item rating matrix is completed by using RBF network based on Cover's theorem on the separability of patterns. RBF network learning is done by unsupervised kernel-based fuzzy c-means clustering algorithm for selecting RBF centers, and supervised gradient descend method for selecting RBF weights. In the offline phase, we predict non-rated items of a user. Then the full rating matrix is used to rank all the users. The ranking is done by solving an eigenvalue problem. This paper overcomes the scalability problem by clustering the users, the sparsity problem by completing the sparse rating matrix, and the new user cold start problem by recommending the top rated items of the high-ranked user. The results of the experiments, on the benchmark data sets, show that the proposed system produces high quality recommendation, in terms of accuracy and quality.

Dual Auto-Encoder Based Rating Prediction Recommendation Algorithm

Collaborative filtering is the most widely used method in recommendation algorithms, but it still faces the serious problem of data sparsity. Traditional collaborative filtering uses matrix decomposition to learn the latent features of users and items. As an extension model of matrix decomposition, Funk-SVD model has attracted wide attention due to its good scalability and easy implementation, but it is difficult to extract the latent features of users and items from sparse rating information because it essentially learns the linear relationship between users and items. To solve this problem, we propose a Dual auto-encoder based Rating Prediction Recommendation Algorithm (DRPRA) model. The DRPRA model uses the strong ability of deep learning in feature learning, which combines double auto-encoders with Funk-SVD. First, the auto-encoder captures the latent features of users and items respectively. Then, the Funk-SVD combines the user features with item features to reconstruct the rating matrix. After that, we minimize the error between original rating matrix and reconstructed rating matrix, and to alleviate the problem of data sparsity and improve the accuracy of rating prediction effectively. We conducted extensive experiments on Movielens-100K, Movie Tweeting-10k, and Film Trust datasets, and the results show that the rating prediction model based on dual auto-encoders has a superior recommendation performance.

Shilling attack detection for collaborative recommender systems: a gradient boosting method.

Organized malicious shilling attackers influence the output of the collaborative filtering recommendation systems by inserting fake users into the rating matrix within the database. The existence of shilling attack poses a serious risk to the stability of the system. To counter this specific security threat, many attack detection methods are proposed. Some of the past methods suffer from two disadvantages, the first being that they only analyze the rating matrix from a single perspective of user rating values and ignore other perspectives. Another is that some methods only use a single classifier to handle the classification of malicious attackers. Considering the above disadvantages, this paper proposes a gradient boosting method (named XGB-SAD) to achieve attack detection by combining double-view and gradient boosting. We first analyze the rating matrix with a double-view of time and item, which in turn defines the TPUS collection. Then our method uses eXtreme Gradient Boosting to perform heuristic iterative optimization of the model's objective function and uses the idea of ensemble learning to integrate multiple sets of base classifiers into strong classifier. The integrated strong classifiers are used to complete the detection of malicious attackers. Finally, we perform several experiments and the results demonstrate that XGB-SAD outperforms the comparison methods in terms of small-scale attack detection and overall detection, which proves the performance of our method.

A Matrix Decomposition Model Based on Feature Factors in Movie Recommendation System

Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF) techniques used in recommender systems due to its effectiveness and ability to deal with very large user-item rating matrix. Among them, matrix decomposition method mainly uses the interactions records between users and items to predict ratings. Based on the characteristic attributes of items and users, this paper proposes a UISVD++ model that fuses the type attributes of movies and the age attributes of users into MF framework. Project and user representations in MF are enriched by projecting each user's age attribute and each movie's type attribute into the same potential factor space as users and items. Finally, the MovieLens-100K and MovieLens-1M datasets were used to compare with the traditional SVD++ and other models. The results show that the proposed model can achieve the best recommendation performance and better predict user ratings under all backgrounds.

Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System

The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before. At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly. In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service. But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from two inherent issues namely, rating sparsity and cold-start. Inspired by the breakthrough results and great strides of deep learning in a wide variety of applications, this work for the first time proposes a deep clustering model leveraging the strength of deep learning and fuzzy clustering for user-based collaborative filtering (UCF). At first, the proposed model applies deep autoencoder (AE) to learn users’ latent feature representation from the original rating matrix and then conducts fuzzy clustering assignments on learnt users’ latent representation to form user clusters. More importantly, the proposed model defines a unified objective function combining the reconstruction loss and clustering loss to yield end-to-end UCF process that can jointly optimize the process of data representation learning and clustering assignment. The effectiveness of the proposed model is evaluated with two real-world datasets, MoiveLens100K and MovieLens1M using two standard performance metrics. Experimental results of the proposed model demonstrated the prospects for gain in top-K recommendation accuracy against recent related works with resistant to rating sparsity and cold start problems.

An Web Service Recommendation Method Based on Location and Decomposition Machine Model

With the increasing number of Web services with similar functions on the Internet, traditional collaborative filtering service recommendation methods may encounter problems such as data sparseness, cold start, and poor scalability. To solve the above problems, this project proposes a new Web service recommendation method based on the decomposition machine model. The method decomposes the user trust relationship matrix and the product rating matrix while adding the geographic location information of the service, and transforms the correlation matrix of the calculated user feature vector and item feature vector into the same latent factor space by means of a decomposition machine. Optimize training model parameters to provide users with accurate prediction scores. The ultimate purpose of QoS prediction is to recommend high-quality services to users, improve the efficiency of users' discovery and selection of high-quality services, and ultimately promote the utilization of network Web services and promote service providers to release higher-quality services. Scientific significance, but also has better application value.

A deep recommendation model of cross-grained sentiments of user reviews and ratings

The matrix factorization model based on user-item rating data has been widely studied and applied in recommender systems. However, data sparsity, the cold-start problem, and poor explainability have restricted its performance. Textual reviews usually contain rich information about items’ features and users’ sentiments and preferences, which can solve the problem of insufficient information from only user ratings. However, most recommendation algorithms that take sentiment analysis of review texts into account are either fine- or coarse-grained, but not both, leading to uncertain accuracy and comprehensiveness regarding user preference. This study proposes a deep learning recommendation model (i.e., DeepCGSR) that integrates textual review sentiments and the rating matrix. DeepCGSR uses the review sets of users and items as a corpus to perform cross-grained sentiment analysis by combining fine- and coarse-grained levels to extract sentiment feature vectors for users and items. Deep learning technology is used to map between the extracted feature vector and latent factor through the rating-based matrix factorization model and obtain deep, nonlinear features to predict the user's rating of an item. Iterative experiments on e-commerce datasets from Amazon show that DeepCGSR consistently outperforms the recommendation models LFM, SVD++, DeepCoNN, TOPICMF, and NARRE. Overall, comparing with other recommendation models, the DeepCGSR model demonstrated improved evaluation results by 14.113% over LFM, 13.786% over SVD++, 9.920% over TOPICMF, 5.122% over DeepCoNN, and 2.765% over NARRE. Meanwhile, the DeepCGSR has great potential in fixing the overfitting and cold-start problems. Built upon previous studies and findings, the DeepCGSR is the state of the art, moving the design and development of the recommendation algorithms forward with improved recommendation accuracy.