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Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users. Matrix factorization is widely used in collaborative filtering algorithms because of its simplicity and efficiency. However, the simple dot-product method cannot establish a nonlinear relationship between user latent features and item latent features or make full use of their personalized information. The model of a neural network combined with an attention mechanism can effectively establish a nonlinear relationship between the potential features of users and items and improve the recommendation accuracy of the model. However, it is difficult for the general attention mechanism algorithm to solve the problem of attention interaction when the number of features between the users and items is not the same. To solve the above problems, this paper proposes an attention interaction matrix factorization (AIMF) model. The AIMF model adopts a symmetric structure using MLP calculation. This structure can simultaneously extract the nonlinear features of user latent features and item latent features, thus reducing the computation time of the model. In addition, an improved attention algorithm named slide-attention is included in the model. The algorithm uses the sliding query method to obtain the user's attention to the latent features of the item and solves the interaction problem among different dimensions of the user, and the latent features of the item. [ABSTRACT FROM AUTHOR]

An improved recommendation algorithm based on Conditional Variational Autoencoder (CVAE) and Constrained Probabilistic Matrix Factorization (CPMF) is proposed to address the issues of poor recommendation performance in traditional user-based collaborative filtering algorithms caused by data sparsity and suboptimal feature extraction. Firstly, in the data preprocessing stage, a hidden layer is added to CVAE, and random noise is introduced into the hidden layer to constrain the data features, thereby obtaining more accurate latent features and improving the model's robustness and generative capability. Secondly, the category of items is incorporated as auxiliary information in CVAE to supervise the encoding and decoding of item data. By learning the distribution characteristics of the data, missing values in the rating data can be effectively reconstructed, thereby reducing the sparsity of the rating matrix. Subsequently, the reconstructed data is processed using CPMF, which optimizes the feature extraction performance by imposing constraints on user features. Finally, the prediction rating of a user for an item can be obtained through the matrix product of user and item feature matrices. Experimental results on the MovieLens-100K and MovieLens-1M datasets demonstrate the effectiveness and superiority of the proposed algorithm over four comparative algorithms, as it exhibits significant advantages in terms of root mean square error and mean absolute error metrics. [ABSTRACT FROM AUTHOR]

Traditional Collaborative filtering (CF) is one of the techniques of recommender systems that has been successfully exploited in various applications, but sometimes they fail to provide accurate recommendations because they depend majorly on the rating matrix, which is always scanty and of very high dimension. Matrix factorization (MF) algorithms are variants of latent factor models, which are easy, fast, and efficient. This article reviews the related research and advances in the application of matrix factorization techniques in recommender systems. Popular matrix factorization algorithms utilized in recommender systems were reviewed. The peculiar challenges of using matrix factorization in recommender systems were also enumerated and discussed with the goal of identifying the different problems solved with the use of matrix factorization techniques as applied in recommender systems. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet medical information technology, a large amount of medical data appeared on the Internet, however, how to extract effective information from the massive and complex medical data to provide professional medical services and suggestions to users has become a hot spot for this research. The recommendation system can effectively solve the problem of accurate matching of complex medical data resources; however, the cold start, data sparsity and user interest migration of the system in the complex data environment have a large impact on the recommendation effect; therefore, this paper proposes a weighted neural matrix decomposition improved health management recommendation scheme incorporating deep learning techniques. The scheme first uses an implicit feedback method to improve the prediction scores and improve the linear model performance of the matrix decomposition algorithm to form a weighted neural matrix decomposition health management recommendation algorithm. Second, the improved method and deep neural network are fused to improve the performance of the nonlinear model part of the algorithm by using the structural properties of the neural network. Finally, this paper's method is compared with the mainstream six recommendation algorithms on four publicly available real datasets. The experimental results show that the root mean square error (RMSE) of the WENMF algorithm is smaller than that of the comparison algorithm on all four datasets, and the convergence speed is faster. The hit rate (HR) and normalized discounted cumulative gain (NDCG) of the WENMF algorithm are higher than those of the comparison algorithm on all four datasets, and the maximum difference is 0.04. Therefore, the recommendation accuracy and ranking quality of the WENMF algorithm in the recommendation system are verified, and the cold start and data sparsity problems of the recommendation system are effectively alleviated. [ABSTRACT FROM AUTHOR]

Due to the ability of knowledge graph to effectively solve the sparsity problem of collaborative filtering, knowledge graph (KG) has been widely studied and applied as auxiliary information in the field of recommendation systems. However, existing KG-based recommendation methods mainly focus on learning its representation from the neighborhood of target items, ignoring the influence of other items on the target item. The learning focuses on the local feature representation of the target item, which is not sufficient to effectively explore the user's preference degree for the target item. To address the above issues, in this paper, an approach combining users' local interest features with global interest features (KGG) is proposed to efficiently explore the user's preference level for the target item, which learns the user's local interest features and global interest features for target item through Knowledge Graph Convolutional Network and Generative Adversarial Network (GAN). Specifically, this paper first utilizes the Knowledge Graph Convolutional Network to mine related attributes on the knowledge graph to effectively capture item correlations and obtain the local feature representation of the target item, then uses the matrix factorization method to learn the user's local interest features for target items. Secondly, it uses GAN to learn the user's global interest features for target items from the implicit interaction matrix. Finally, a linear fusion layer is designed to effectively fuse the user's local and global interests towards target items to obtain the final click prediction. Experimental results on three real datasets show that the proposed method not only effectively integrates the user's local and global interests but also further alleviates the problem of data sparsity. Compared with the current baselines for knowledge graph-based systems, the KGG method achieves a maximum improvement of 8.1% and 7.6% in AUC and ACC, respectively. [ABSTRACT FROM AUTHOR]

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dotproduct operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset. [ABSTRACT FROM AUTHOR]

One of the most prevalent recommendation systems is ranking-oriented collaborative filtering which employs ranking aggregation. The collaborative filtering study recently applied the ranking aggregation that considers the weight point of items to achieve a more accurate recommended ranking. However, this algorithm suffers in the execution time with an increased number of items. Therefore, this study proposes a new recommendation algorithm that combines the matrix decomposition method and ranking aggregation to reduce the time complexity. The matrix decomposition method utilizes singular decomposition value (SVD) to predict the unrated items. The ranking aggregation method applies weight point rank (WPR) to obtain the recommended items. The experimental results with the MovieLens 100K dataset result in a faster running time of 13.502 s. In addition, the normalized discounted cumulative gain (NDCG) score increased by 27.11% compared to the WP-Rank algorithm. [ABSTRACT FROM AUTHOR]

In recent years, hybrid recommendation techniques based on feature fusion have gained extensive attention in the field of list ranking. Most of them fuse linear and nonlinear models to simultaneously learn the linear and nonlinear features of entities and jointly fit user-item interactions. These methods are based on implicit feedback, which can reduce the difficulty of data collection and the time of data preprocessing, but will lead to the lack of entity interaction depth information due to the lack of user satisfaction. This is equivalent to artificially reducing the entity interaction features, limiting the overall performance of the model. To address this problem, we propose a two-stage recommendation model named A-DNR, short for Attention-based Deep Neural Ranking. In the first stage, user short-term preferences are modeled through an attention mechanism network. Then the user short-term preferences and user long-term preferences are fused into dynamic user preferences. In the second stage, the high-order and low-order feature interactions are modeled by a matrix factorization (MF) model and a multi-layer perceptron (MLP) model, respectively. Then, the features are fused through a fully connected layer, and the vectors are mapped to scores. Finally, a ranking list is output through the scores. Experiments on three real-world datasets (Movielens100K, Movielens1M and Yahoo Movies) show that our proposed model achieves significant improvements compared to existing methods. [ABSTRACT FROM AUTHOR]

Cold Start problems in recommender systems pose various challenges in the adoption and use of recommender systems, especially for new item uptake and new user engagement. This restricts organizations to realize the business value of recommender systems as they have to incur marketing and operations costs to engage new users and promote new items. Owing to this, several studies have been done by recommender systems researchers to address the cold start problems. However, there has been very limited recent research done on collating these approaches and algorithms. To address this gap, the paper conducts a systematic literature review of various strategies and approaches proposed by researchers in the last decade, from January 2010 to December 2021, and synthesizes the same into two categories: data-driven strategies and approach-driven strategies. Furthermore, the approach-driven strategies are categorized into five main clusters based on deep learning, matrix factorization, hybrid approaches, or other novel approaches in collaborative filtering and content-based algorithms. The scope of this study is limited to a systematic literature review and it does not include an experimental study to benchmark and recommend the best approaches and their context of use in cold start scenarios. [ABSTRACT FROM AUTHOR]

The most algorithms for Recommender Systems (RSs) are based on a Collaborative Filtering (CF) approach, in particular on the Probabilistic Matrix Factorization (PMF) method. It is known that the PMF method is quite successful for the rating prediction. In this study, we consider the problem of rating prediction in RSs. We propose a new algorithm which is also in the CF framework; however, it is completely different from the PMF-based algorithms. There are studies in the literature that can increase the accuracy of rating prediction by using additional information. However, we seek the answer to the question that if the input data does not contain additional information, how we can increase the accuracy of rating prediction. In the proposed algorithm, we construct a curve (a low-degree polynomial) for each user using the sparse input data and by this curve, we predict the unknown ratings of items. The proposed algorithm is easy to implement. The main advantage of the algorithm is that the running time is polynomial, namely it is ϑ(n2), for sparse matrices. Moreover, in the experiments we get slightly more accurate results compared to the known rating prediction algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, recommender systems are vital in lessening the information overload by filtering out unnecessary information, thus increasing comfort and quality of life. Matrix factorization (MF) is a well-known recommender system algorithm that offers good results but requires a certain level of system knowledge and some effort on part of the user before use. In this article, we proposed an improvement using grammatical evolution (GE) to automatically initialize and optimize the algorithm and some of its settings. This enables the algorithm to produce optimal results without requiring any prior or in-depth knowledge, thus making it possible for an average user to use the system without going through a lengthy initialization phase. We tested the approach on several well-known datasets. We found our results to be comparable to those of others while requiring a lot less set-up. Finally, we also found out that our approach can detect the occurrence of over-saturation in large datasets. [ABSTRACT FROM AUTHOR]

User rating of a service is the explicit behavior of users expressing their preference for the service. Most exciting recommendation methods focus on predicting user‐service ratings according to users' historical rating behaviors. However, the behavior of users invoking services is implicit feedback. By analyzing the services called by users, mining their potential semantic representations can also help model users' hidden interests. To this end, how to integrate the implicit feedback and explicit rating of users to provide users with better recommendation experience is a problem to be addressed for service recommendation. In this paper, we propose a novel latent semantic integrated explicit rating (LSIER) scheme to recommend services to users. The LSIER scheme is designed by integrating the probabilistic matrix factorization (PMF) model and the probabilistic latent semantic index (PLSI) model. consists of the two stages: (1) the PMF model is used to generate a user feature matrix and a service feature matrix, and the two feature matrices are updated to complete the missing service score records of the users, and (2) the PLSI model is used to train users access records, where an expectation maximization algorithm is applied to derive the model parameters to realize unsupervised soft clustering of services. When the user gives explicit or implicit feedback to the service, the LSIER scheme can identify the current interest probability distribution of the user according to the category to which the called service belongs, and provide the user with a list of service recommendations with scores. The performance of the proposed LSIER scheme is evaluated using the Netflix data set and the Movielens data set. Experiments show that the scheme can achieve better recommendation accuracy and recall rate than existing methods. [ABSTRACT FROM AUTHOR]

Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results. [ABSTRACT FROM AUTHOR]

The function of recommendation system is to help users actively finding personalized items that meet theirs preferences and recommend them to users. Collaborative filtering algorithm is a classic algorithm in recommender system, but it is limited by cold start of data and sparsity and has disadvantages such as poor interpretability and poor model generalization ability . This paper studied its shortcomings. By taking the original score matrix in the form of user-project bipartite graph as input, designing the figure convolution neural network as a variant of graph autoencoder, which was obtained by the latent vector of user and item by iteratively aggregating neighbor node information, and combining CNN, this paper proposed a recommendation algorithm based on convolution matrix decomposition to improve the interpretability of the model and generalization ability. And also solved the auxiliary information fusion the data sparseness, and made recommendation performance improved by 1.4% and 1.7% . It provides a new idea for recommendation direction based on graph neural network in the future. [ABSTRACT FROM AUTHOR]

In mobile crowdsensing, finding the best match between tasks and users is crucial to ensure both the quality and effectiveness of a crowdsensing system. Existing works usually assume a centralized task assignment by the crowdsensing platform, without addressing the need of fine-grained personalized task matching. In this paper, we argue that it is essential to match tasks to users based on a careful characterization of both the users’ preference and reliability. To that end, we propose a personalized task recommender system for mobile crowdsensing, which recommends tasks to users based on a recommendation score that jointly takes each user’s preference and reliability into consideration. We first present a hybrid preference metric to characterize users’ preference by exploiting their implicit feedback. Then, to profile users’ reliability levels, we formalize the problem as a semi-supervised learning model, and propose an efficient block coordinate descent algorithm to solve the problem. For some tasks that lack users’ historical information, we further propose a matrix factorization method to infer the users’ reliability levels on those tasks. We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate that our system can achieve superior performance to the benchmarks in both user profiling and personalized task recommendation. [ABSTRACT FROM AUTHOR]

Application of the recommender system on the online platform turns out to produce a large volume of data and at an unexpected rate, making it more realistic to study the recommender system under streaming settings. Many studies have tried to develop a streaming recommender system, one of which is Stream-centered Probabilistic Matrix Factorization (SPMF). In SPMF, model will be updated continuously every time a data stream window enters the system using the data managed in reservoir plus the data in data stream window. This study focuses on developing a reservoir sampling technique in SPMF method, namely uniform random sampling, which can result in model ranking errors due to the loss of observational data from memory. To reduce this consequence, sample is selected based on the matrix factorization score, which is considered to represent the popularity of item. In addition, this research also developed a parallelization technique for the updating of the SPMF algorithm for the purpose of increasing scalability. Based on the experiments conducted on E-commerce dataset, the proposed method is proven to have better recommendation quality than the baseline method and reaches at least 84% quality of BPR-MF with a reduction of the dataset up to 78% and computation time 60 times faster. Parallel computing can also increase computation speed by 23% by using 4 workers. [ABSTRACT FROM AUTHOR]

Recommendation systems, social network analysis, medical imaging, and data mining often involve processing sparse high-dimensional data. Such high-dimensional data are naturally represented as tensors, and they cannot be efficiently processed by conventional matrix or vector computations. Sparse Tucker decomposition is an important algorithm for compressing and analyzing these sparse high-dimensional datasets. When energy efficiency and data privacy are major concerns, hardware accelerators on resource-constraint platforms become crucial for the deployment of tensor algorithms. In this work, we propose a hybrid computing framework containing CPU and FPGA to accelerate sparse Tucker factorization. This algorithm has three main modules: 1) tensor-times-matrix (TTM); 2) Kronecker products; and 3) QR decomposition with column pivoting (QRP). In addition, we accelerate the former two modules on a Xilinx FPGA and the latter one on a CPU. Our hybrid platform achieves $23.6 \times \sim 1091\times $ speedup and over 93.519% ~ 99.514% energy savings compared with CPU on the synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]

Matrix factorization (MF) models are effective and easy to expand and are widely used in industry, such as rating prediction and item recommendation. The basic MF model is relatively simple. In practical applications, side information such as attributes or implicit feedback is often combined to improve accuracy by modifying the model and optimizing the algorithm. In this paper, we propose an attribute interaction-aware matrix factorization (AIMF) method for recommendation tasks. We partition the original rating matrix into different sub-matrices according to the attribute interactions, train each sub-matrix independently, and merge all the latent vectors to generate the final score. Since the generated sub-matrices vary in size, an adaptive regularization coefficient optimization strategy and an adaptive latent vector dimension optimization strategy are proposed for sub-matrix training, and a variety of latent vector merging methods are put forward. The method AIMF has two advantages. When the original rating matrix is particularly large, the training time complexity of the MF-based model becomes higher and the update cost of the model is also higher. In AIMF, because each sub-matrix is usually much smaller than the original rating matrix, the training time complexity is greatly reduced after using parallel computing technology. Secondly, in AIMF, it is not necessary to modify the matrix factorization model to incorporate attributes and their interactive information into the model to improve the performance. The experimental results on the two classic public datasets MovieLens 1M and MovieLens 100k show that AIMF can not only effectively improve the accuracy of recommendation, but also make full use of parallel computing technology to improve training efficiency without modifying the matrix factorization model. [ABSTRACT FROM AUTHOR]

Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and analysis of multiple data sources and helps discover meaningful information. Nonnegative CMTF (N-CMTF) has been employed in many applications for identifying latent patterns, prediction, and recommendation. However, due to the added complexity with coupling between tensor and matrix data, existing N-CMTF algorithms exhibit poor computation efficiency. In this paper, a computationally efficient N-CMTF factorization algorithm is presented based on the column-wise element selection, preventing frequent gradient updates. Theoretical and empirical analyses show that the proposed N-CMTF factorization algorithm is not only more accurate but also more computationally efficient than existing algorithms in approximating the tensor as well as in identifying the underlying nature of factors. [ABSTRACT FROM AUTHOR]

• A triple cross-domain collaborative filtering recommendation method is proposed. • An extended codebook construction algorithm is designed. • A transfer learning method based on extended codebook is presented. • Empirical results show the proposed method is superior to state-of-the-art methods. Recently, data sparsity is still one of the critical problems faced by recommendation systems. Although many existing methods based on cross-domain can alleviate it to a certain extent, these methods only use the information of single-domain (e.g., user-side, item-side and rating-side) or dual-domain (e.g., user-rating-side, user-item-side and item-rating-side) to make recommendations, which results in performance degradation. In this paper, we propose a triple cross-domain collaborative filtering method to alleviate data sparsity, named TCD-CF. In TCD-CF method, the triple-side intrinsic characteristics are first obtained by using the joint nonnegative matrix factorization to integrate the user-side, item-side and rating-side domain knowledge. Then the extended codebook (as knowledge to transfer) based on these intrinsic characteristics is constructed by using the orthogonal nonnegative matrix tri-factorization. Finally, the codebook-based transfer method for cross-system CF is applied into the source domain and target domain to predict the missing ratings and perform recommendation in the target domain. Extensive experiments on two real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods for the cross-domain recommendation task. [ABSTRACT FROM AUTHOR]

Deep matrix factorization adopts deep nonlinear mapping to break through the bottleneck of bilinear relationship affecting the performance of the recommendation system in matrix factorization. However, it doesn't take into account users' preference for unrated items, and its recommendation performance isn't advantageous for large-scale data with high sparsity. This paper proposed a recommendation algorithm for fusing matrix completion and deep matrix factorization. First, it filled the unknown elements in the original scoring matrix by the matrix completion model. Then, based on the completed matrix, it constructed the latent vectors of users and items by deep learning model. Finally, this paper tested the proposed model on the MovieLens and SUSHI datasets. Experimental results show that the proposed algorithm significantly improves the performance of the recommendation system compared to deep matrix factorization. [ABSTRACT FROM AUTHOR]

Probabilistic matrix factorization (PMF) is the most popular method among low-rank matrix approximation approaches that address the sparsity problem in collaborative filtering for recommender systems. PMF depends on the classical maximum a posteriori estimator for estimating model parameters; however, these approaches are vulnerable to overfitting because of the nature of a single point estimation that is pursued by these models. An alternative approach to PMF is a Bayesian PMF model that suggests the Markov chain Monte Carlo algorithm as a full estimation for approximate intractable posterior over model parameters. However, despite its success in increasing prediction, it has a high computational cost. To this end, we proposed a novel Bayesian deep learning-based model treatment, namely, variational autoencoder Bayesian matrix factorization (VABMF). The proposed model uses stochastic gradient variational Bayes to estimate intractable posteriors and expectation–maximization-style estimators to learn model parameters. The model was evaluated on the basis of three MovieLens datasets, namely, Ml-100k, Ml-1M, and Ml-10M. Experimental results showed that our proposed VABMF model significantly outperforms state-of-the-art RS. [ABSTRACT FROM AUTHOR]

Binary representation of users and items can dramatically improve efficiency of recommendation and reduce size of recommendation models. However, learning optimal binary codes for them is challenging due to binary constraints, even if squared loss is optimized. In this article, we propose a general framework for discrete matrix factorization based on discrete optimization, which can 1) optimize multiple loss functions; 2) handle both explicit and implicit feedback datasets; and 3) take auxiliary information into account without any hyperparameters. To tackle the challenging discrete optimization problem, we propose block coordinate descent based on semidefinite relaxation of binary quadratic programming. We theoretically show that it is equivalent to discrete coordinate descent when only one coordinate is in each block. We extensively evaluate the proposed algorithms on eight real-world datasets. The results of evaluation show that they outperform the state-of-the-art baselines significantly and that auxiliary information of items improves recommendation performance. For better showing the advantages of binary representation, we further propose a two-stage recommender system, consisting of an item-recalling stage and a subsequent fine-ranking stage. Its extensive evaluation shows hashing can dramatically accelerate item recommendation with little degradation of accuracy. [ABSTRACT FROM AUTHOR]

Recommendation systems are successful personalizing tools and information filtering in web. One of the most important recommendation methods is matrix factorization method. In matrix factorization method, the latent features of users and items are determined in such a way that the inner product of the latent features of a user with the latent features of an item is equal to that user's rating on that item. This model is solved using alternate optimization algorithm. The solution and the prediction error of this algorithm depend on the initial values of the latent features of users which are usually set to small random values. The purpose of this paper is to propose a fast alternate optimization algorithm for matrix factorization which converges to a good solution. To do so, firstly, we show experimentally that if the latent feature vector of each user is initialized by a vector of which elements are equal, we can also obtain a proper solution using the alternate optimization algorithm. Then, we prove that if our proposed initialization method is used, the alternate optimization algorithm for matrix factorization can be simplified using Sherman–Morrison formula. Experimental results on 5 real datasets show that the runtime of our proposed algorithm is 2–45 times less than the traditional method. [ABSTRACT FROM AUTHOR]

With the widespread use of social networks, social recommendation algorithms that add social relationships between users to recommender systems have been widely applied. Existing social recommendation algorithms only introduced one type of social relationship to the recommendation system, but in reality, there are often multiple social relationships among users. In this paper, a new matrix factorization recommendation algorithm combined with multiple social relationships is proposed. Through experiment results analysis on the Epinions dataset, the proposed matrix factorization recommendation algorithm has a significant improvement over the traditional and matrix factorization recommendation algorithms that integrate a single social relationship. [ABSTRACT FROM AUTHOR]

Matrix factorization (MF) has earned great success on recommender systems. However, the common-used regression-based MF not only is sensitive to outliers but also unable to guarantee that the predicted values are in line with the user preference orders, which is the basis of common measures of recommender systems, e.g., nDCG. To overcome the aforementioned drawbacks, we propose a framework for personalized ranking of Poisson factorization that utilizes learning-to-rank based posteriori instead of the classical regression-based ones. Owing to the combination, the proposed framework not only preserves user preference but also performs well on a sparse matrix. Since the posteriori that combines learning to rank and Poisson factorization does not follow the conjugate prior relationship, we estimate variational parameters approximately and propose two optimization approaches based on variational inference. As long as the used learning-to-rank model has the 1st and 2nd order partial derivatives, by exploiting our framework, the proposed optimizing algorithm can maximize the posteriori whichever the used learning-to-rank model is. In the experiment, we show that the proposed framework outperforms the state-of-the-art methods and achieves promising results on consuming log and rating datasets for multiple recommendation tasks. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of protecting privacy in recommender systems. We focus on protecting the items rated by users and propose a novel privacy-preserving matrix factorization algorithm. In our algorithm, the user will submit a fake gradient to make the central server not able to distinguish which items are selected by the user. We make the Kullback–Leibler distance between the real and fake gradient distributions to be small thus hard to be distinguished. Using theories and experiments, we show that our algorithm can be reduced to a time-delay SGD, which can be proved to have a good convergence so that the accuracy will not decline. Our algorithm achieves a good tradeoff between the privacy and accuracy. [ABSTRACT FROM AUTHOR]

A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. Additionally, there are the context‐aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including contextual and opinion information. In a previous work, we proposed a context‐aware recommendation method based on text mining (CARM‐TM). The method includes two techniques to extract context from reviews: CIET.5embed, a technique based on word embeddings; and RulesContext, a technique based on association rules. In this work, we have extended our previous method by including CEOM, a new technique which extracts context by using aspect‐based opinions. We call our extension of CARM‐TOM (context‐aware recommendation method based on text and opinion mining). To generate recommendations, our method makes use of the CAMF algorithm, a context‐aware recommender based on matrix factorization. To evaluate CARM‐TOM, we ran an extensive set of experiments in a dataset about restaurants, comparing CARM‐TOM against the MF algorithm, an uncontextual recommender system based on matrix factorization; and against a context extraction method proposed in literature. The empirical results strongly indicate that our method is able to improve a context‐aware recommender system. [ABSTRACT FROM AUTHOR]

The recommender system predicts user preferences by mining user historical behavior data. This paper proposes a social recommendation combining trust relationship and distance metric factorization. On the one hand, the recommender system has a cold start problem, which can be effectively alleviated by adding social relations. Simultaneously, to improve the problem of sparse trust matrix, we use the Jaccard similarity coefficient and the Dijkstra algorithm to reconstruct the trust matrix and explore the potential user trust relationship. On the other hand, the traditional matrix factorization algorithm is modeled by the user item potential factor dot product, however, it does not satisfy the triangle inequality property and affects the final recommender effect. The primary motivator behind our approach is to combine the best of both worlds, mitigate the inherent weaknesses of each paradigm. Combining the advantages of the two ideas, it has been demonstrated that our algorithm can enhance recommender performance and improve cold start in recommender systems. [ABSTRACT FROM AUTHOR]

The main purpose of collaborative filtering algorithm is to provide a personalized recommender system based on past interactions of each user (e.g., clicks and purchases). Among various collaborative filtering techniques, matrix factorization is widely adopted in diverse applications. This technique has superior characteristics, including applying latent feature vectors to represent users or items and projecting users and items into a shared latent feature space. In the present study, a matrix factorization model with the neural embedding called the neural embedding collaborative filtering (NECF) is proposed. In order to evaluate the performance of the proposed method, a probabilistic auto-encoder is initially applied to achieve unsupervised learning to generate the neural embedding vector from the user–item data. Secondly, these vectors are combined with a regression model based on single point negative sampling to represent the latent feature vectors of the user with regression coefficients. Moreover, an inner product is applied on latent features of users and items to determine the correlations between them. It should be indicated that the NECF is generic so that it can express and generalize the matrix factorization under its framework. In the present study, a ridge regression learning is applied on latent features of each user. The experimental results on two benchmark data sets show that the proposed model outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The traditional matrix factorization algorithm uses all the training data as a whole to obtain user interest features from the perspective of time, ignoring the drift of user interest. To address this problem, this paper proposed a recommendation algorithm based on state space model and probability matrix factorization. Firstly, this method mapped the matrix factorization of user interest feature vector and user rating data into linear Gaussian state space. Then, it used EM algorithm and Kalman filter to solve the model parameters dynamically. Finally, it obtained the user interest feature vector to confirm whether the user interest drifts or not, and made the recommendation. The experiments demonstrate that the proposed algorithm can better perceive user interest drift and improve the recommendation quality compared with the traditional matrix factorization algorithm. [ABSTRACT FROM AUTHOR]

A current problem in diet recommendation systems is the matching of food preferences with nutritional requirements, taking into account individual characteristics, such as body weight with individual health conditions, such as diabetes. Current dietary recommendations employ association rules, content-based collaborative filtering, and constraint-based methods, which have several limitations. These limitations are due to the existence of a special user group and an imbalance of non-simple attributes. Making use of traditional dietary recommendation algorithm researches, we combine the Adaboost classifier with probabilistic matrix factorization. We present a personalized diet recommendation algorithm by taking advantage of probabilistic matrix factorization via Adaboost. A probabilistic matrix factorization method extracts the implicit factors between individual food preferences and nutritional characteristics. From this, we can make use of those features with strong influence while discarding those with little influence. After incorporating these changes into our approach, we evaluated our algorithm's performance. Our results show that our method performed better than others at matching preferred foods with dietary requirements, benefiting user health as a result. The algorithm fully considers the constraint relationship between users' attributes and nutritional characteristics of foods. Considering many complex factors in our algorithm, the recommended food result set meets both health standards and users' dietary preferences. A comparison of our algorithm with others demonstrated that our method offers high accuracy and interpretability. [ABSTRACT FROM AUTHOR]

We propose a practical algorithm for low rank matrix completion for matrices with binary entries which obtains explicit binary factors and show it performs well at the recommender task on real world datasets. The algorithm, which we call TBMC (Tiling for Binary Matrix Completion), gives interpretable output in the form of binary factors which represent a decomposition of the matrix into tiles. Our approach extends a popular algorithm from the data mining community, PROXIMUS, to missing data, applying the same recursive partitioning approach. The algorithm relies upon rank-one approximations of incomplete binary matrices, and we propose a linear programming (LP) approach for solving this subproblem. We also prove a 2-approximation result for the LP approach which holds for any subsampling pattern, and show that TBMC exactly solves the rank- k prediction task for a underlying block-diagonal tiling structure with geometrically decreasing tile sizes, providing the ratio between successive tiles is less than 1 / 2. Numerical experiments show that TBMC outperforms existing methods on real datasets. [ABSTRACT FROM AUTHOR]

Summary: With the rapid proliferation of information and communication technology (ICT), the vast amount of available data creates information overload. The Websites and e‐commerce applications employ several information filtering methods such as personalized recommender system to manage the information overload. The recommender system assists the users in obtaining the desired list of products based on their interest. Several existing research works focus on the novelty or unexpectedness in the recommendation list while ensuring the quality to enhance the recommendation mechanism. It is essential to balance the unexpected and useful products or services to generate the satisfactory personalized recommendations with novelty. Thus, this paper proposes a novelty‐driven movie suggestion using integrated matrix factorization and temporal‐aware clustering optimization (NOMINATE). The proposed approach determines the personalized preferences through probabilistic matrix factorization (PMF) and contextually updates the rules and extracts the user preferences based on the inherent features of both the users and movies with temporal information. The NOMINATE approach also suggests the novelty‐driven, and desired top‐N movies to the users through the K‐means, and particle swarm optimization (PSO)‐based clustering algorithm with the help of LOD source. To identify the expert users, the NOMINATE approach applies the K‐means and PSO‐based clustering algorithm to enrich the personalized features of the users. Moreover, it integrates the relevant features with the preferred set of features for each user using the LOD source and decides a set of optimal preferences of the users. Finally, the NOMINATE approach generates the top‐N recommendation list for the corresponding user through ranking method. The experiment results stipulate that the NOMINATE approach personalize the top‐N movie recommendations with high performance regarding accuracy and novelty when compared with the existing recommendation method. [ABSTRACT FROM AUTHOR]

The problem of predicting students' performance has been recently tackled by using matrix factorization, a popular method applied for collaborative filtering based recommender systems. This problem consists of predicting the unknown performance or score of a particular student for a task s/he did not complete or did not attend, according to the scores of the tasks s/he did complete and the scores of the colleagues who completed the task in question. The solving method considers matrix factorization and a gradient descent algorithm in order to build a prediction model that minimizes the error in the prediction of test data. However, we identified two key aspects that influence the accuracy of the prediction. On the one hand, the model involves a pair of important parameters: the learning rate and the regularization factor, for which there are no fixed values for any experimental case. On the other hand, the datasets are extracted from virtual classrooms on online campuses and have a number of implicit latent factors. The right figures are difficult to ascertain, as they depend on the nature of the dataset: subject, size, type of learning, academic environment, etc. This paper proposes some approaches to improve the prediction accuracy by optimizing the values of the latent factors, learning rate, and regularization factor. To this end, we apply optimization algorithms that cover a wide search space. The experimental results obtained from real-world datasets improved the prediction accuracy in the context of a thorough search for predefined values. Obtaining optimized values of these parameters allows us to apply them to further predictions for similar datasets. [ABSTRACT FROM AUTHOR]

One of the primary tasks for commercial recommender systems is to predict the probabilities of users clicking items, e.g., advertisements, music and products. This is because such predictions have a decisive impact on profitability. The classic recommendation algorithm, collaborative filtering (CF), still plays a vital role in many industrial recommender systems. However, although straight CF is good at capturing similar users' preferences for items based on their past interactions, it lacks regarding (1) modeling the influences of users' sequential patterns from their individual history interaction sequences and (2) the relevance of users' and items' attributes. In this work, we developed an attention-based latent information extraction network (ALIEN) for click-through rate prediction, to integrate (1) implicit user similarity in terms of click patterns (analogous to CF), and (2) modeling the low and high-order feature interactions and (3) historical sequence information. The new model is based on the deep learning, which goes beyond the capabilities of econometric approaches, such as matrix factorization (MF) and k-means. In addition, the approach provides explainability to the recommendation by interpreting the contributions of different features and historical interactions. We have conducted experiments on real-world datasets that demonstrate considerable improvements over strong baselines. [ABSTRACT FROM AUTHOR]

The context-aware recommender system is a research hotspot in the current recommendation field. The CAMF-CC is an effective model in the current field. In view of the low accuracy of the CAMF-CC algorithm in recommendation, this paper presented a new algorithm called ICAMF-CC, which was based on CAMF-CC and incorporated item bias and context weights to predict scores. The advantage of ICAMF-CC was that, on the one hand, it incorporated the influence of the item's own characteristics on the score; on the other hand, it fully considered that different contextual factors had different influences in the recommendation process and improved the accuracy of the rating prediction. Experiments on the LD0S-CoMoDa dataset show that ICAMF-CC is superior to the CAMF-CC algorithm in accuracy. [ABSTRACT FROM AUTHOR]

Matrix factorization is one of the most important algorithms in recommendation systems due to its simple and reliable characteristics. However, since the inner product can't fully capture the interaction between users and items, it is difficult to further enhance the performance of matrix factorization. In order to solve this problem, this paper modified the basic distance metric factorization model and proposed a collaborative filtering recommendation algorithm based on biased metric factorization and implicit feedback,and dynamically modeled users' rating timestamps, which further improved the model performance. This paper tested the most common rating prediction task in the recommendation system on three data sets. The experimental results show that the prediction accuracy of the proposed model is significantly improved. [ABSTRACT FROM AUTHOR]

Based on matrix factorization techniques, this paper implemented a modified binomial matrix decomposition algorithm in order to solve the recommender system's rating prediction problem. It supposed that the user's rating of the item was based on the binomial distribution. There were differences in the user's rating habits, popularity of the items, it would result in an offset in the user-item scoring matrix. This paper used the maximum a posteriori estimate to design model and optimized the model by a stochastic gradient descent algorithm. The experimental results show that the modified binomial matrix decomposition algorithm is superior to the traditional binomial matrix decomposition algorithm in terms of recommender accuracy and offline calculation time on the MovieLens lOOK datasets. [ABSTRACT FROM AUTHOR]

Matrix factorization has been widely applied to various applications. With the fast development of storage and internet technologies, we have been witnessing a rapid increase of data. In this paper, we propose new algorithms for matrix factorization with the emphasis on efficiency. In addition, most existing methods of matrix factorization only consider a general smooth least square loss. Differently, many real-world applications have distinctive characteristics. As a result, different losses should be used accordingly. Therefore, it is beneficial to design new matrix factorization algorithms that are able to deal with both smooth and non-smooth losses. To this end, one needs to analyze the characteristics of target data and use the most appropriate loss based on the analysis. We particularly study two representative cases of low-rank matrix recovery, i.e., collaborative filtering for recommendation and high dynamic range imaging. To solve these two problems, we respectively propose a stage-wise matrix factorization algorithm by exploiting manifold optimization techniques. From our theoretical analysis, they are both are provably guaranteed to converge to a stationary point. Extensive experiments on recommender systems and high dynamic range imaging demonstrate the satisfactory performance and efficiency of our proposed method on large-scale real data. [ABSTRACT FROM AUTHOR]

The traditional matrix decomposition model cannot fully explored the intrinsic relationship between the user and the object in the mean, bias and characteristics. This paper proposed a fitting matrix model to improve the prediction performance by constructing the user and the item matrix to represent the characteristics of the user and the item respectively. The matrix decomposition model had the advantage of accuracy in the field of recommender system, but the gradient descent method, which was the most popular method to train parameters of model, had a slow convergence speed. To resolve the above defects, this paper considered to accelerate the convergence speed using the convergence of quasi Newton method, and named the proposed algorithm as fitting matrix and two orders fusion iterative (FAST) algorithm. The experimental results show that the FAST algorithm is better than the traditional non negative matrix decomposition (NMF), singular value matrix decomposition (SVD), and the regularized singular value matrix decomposition (RSVD). FAST algorithm has a decrease with regard to the mean absolute error (MAE) and the root mean square error (RMSE), and has a significant improvement in the iterative efficiency, which alleviates the problem that the accuracy is difficult to balance with the efficiency of the iteration. [ABSTRACT FROM AUTHOR]

Aiming at the problem of defects in the accuracy of recommendation and data sparse processing for recommendations made by explicit feedback information. This paper introduced implicit feedback information, designed and implemented a multi-dimensional recommendation algorithm( iMCF) based on implicit feedback. The algorithm included three dimensional information of user, project and implicit feedback. For the first two dimensional information, it used the similarity of cloud model to model. For the information of implicit feedback, it mainly used the probabilistic matrix decomposition model to deal with. Afterwards, it balanced the prediction scores obtained from the three dimensions according to the weights, and obtained the final prediction score and made recommendation. The experimental data shows that the algorithm's performance in terms of recall rate and accuracy is significantly improved compared to other algorithms, and it is suitable for big data environments. [ABSTRACT FROM AUTHOR]

Users have online access to millions of audio tracks and movies. Online streaming platforms widely use AI-based recommender systems to help users choose the songs to listen to and the movies to watch. As the volume of available content rises, a standardized methodology to evaluate recommender systems is required. This paper focuses on collaborative-based filtering method of recommender systems. Leveraging the matrix factorization technique, we provide comprehensive information on an algorithm for improving prediction accuracy using standardized Python code and validated with the MovieLens data set. [ABSTRACT FROM AUTHOR]

Most of the existing modeling methods of combining metadata are based on the same user I item attribute weights, so that the key relationships between users and items are not significant, and it is difficult to obtain better recommendation performance. To solve the above problems, this paper proposed a method of deep joint learning recommendation based on metadata and attention mechanism. It used double deep network joint learning, one of the networks implemented matrix nonlinear decomposition based on implicit feedback data to learn user/ project personalization relationship, and another network automatically captured influence the user/ item key attributes to recommend by using attention mechanism, through the user preference relation with weighted of different attributes modeling highlighted the extended model. Experimental results show that the proposed recommendation algorithm has better recommendation performance on two public datasets of MovieLens 100K and MovieLens lM. [ABSTRACT FROM AUTHOR]

To alleviate the challenges posed by privacy protection, sparse data, cold-start problem and weak interpretation, in this paper, we investigate how to uncover the implicit "social relationships" for user/item pairs and create recommendation relationships between users and items in the recommendation systems with no explicit trust relationships. Firstly, according to complex network theory, a recommendation system is presented by a heterogeneous network with five various types of subnets. Secondly, a novel algorithm ISimRank-ISR is designed based on the improved SimRank similarity index, which mainly integrates common and non-common behavior information, as well as attribute information for exploring the implicit social relations, and generates two types of social subnets. Further, introducing these implicit social relations into the nonnegative matrix factorization (NMF) model, another novel algorithm INMF-RRM is proposed to reconstruct the user-item rating subnet. Then, the integration of the above two algorithms derives an algorithm ISR-RRM to create recommendation relationships for the recommendation task. Finally, numerical experiments on two Movielens datasets are conducted to verify the feasibility and effectiveness of the three new algorithms mentioned above, and we come to the following conclusions. (1) The ISimRank-ISR algorithm provides an in-depth exploration of the implicit social relationships for user/item pairs. (2) The INMF-RRM algorithm has convergence properties. (3) To perform better and obtain more accurate recommendations, with the ISR-RRM algorithm, there are optimal parameters achieved. (4) Compared with the other seven benchmark algorithms, the ISR-RRM algorithm shows excellent performance, and its RMSE and MAE are reduced by a maximum of 14.75% and 8.05%, respectively. Additionally, it is more effective in alleviating the cold-start problem. [ABSTRACT FROM AUTHOR]

This paper describes a new collaborative filtering recommendation algorithm based on probability matrix factorization. The proposed algorithm decomposes the rating matrix into two nonnegative matrixes using a predictive rating model. After normalization processing, these two nonnegative matrixes provide useful probability semantics. The posterior distribution of the real part of the probability model is calculated by the variational inference method. Finally, the preferences for items that users have not rated can be predicted. The user–item rating matrix is supplemented by a preference prediction value, resulting in a dense rating matrix. Finally, time weighting is integrated into the rating matrix to construct the 3D user–item–time model, which gives the recommendation results. According to experiments using open Netflix, MovieLens, and Epinion datasets, the proposed algorithm is superior to several existing recommendation algorithms in terms of rating predictions and recommendation effects. [ABSTRACT FROM AUTHOR]

The task and worker recommendation problems in crowdsourcing systems have brought up unique characteristics that are not present in traditional recommendation scenarios, i.e., the huge flow of tasks with short lifespans, the importance of workers' capabilities, and the quality of the completed tasks. These unique features make traditional recommendation approaches no longer satisfactory for task and worker recommendation in crowdsourcing systems. In this article, we propose a two-tier data representation scheme (defining a worker--category suitability score and a worker--task attractiveness score) to support personalized task and worker recommendations. We also extend two optimization methods, namely least mean square error and Bayesian personalized rank, to better fit the characteristics of task/worker recommendation in crowdsourcing systems.We then integrate the proposed representation scheme and the extended optimizationmethods along with the two adapted popular learning models, i.e., matrix factorization and kNN, and result in two lines of top-N recommendation algorithms for crowdsourcing systems: (1) Top-N-Tasks recommendation algorithms for discovering the top-N most suitable tasks for a given worker and (2) Top-N-Workers recommendation algorithms for identifying the top-N best workers for a task requester. An extensive experimental study is conducted that validates the effectiveness and efficiency of a broad spectrum of algorithms, accompanied by our analysis and the insights gained. [ABSTRACT FROM AUTHOR]

Collaborative filtering is the most popular approach when building recommender systems, but the large scale and sparse data of the user-item matrix seriously affect the recommendation results. Recent research shows the user's social relations information can improve the quality of recommendation. However, most of the current social recommendation algorithms only consider the user's direct social relations, while ignoring potential users' interest preference and group clustering information. Moreover, project attribute is also important in item rating. We propose a recommendation algorithm which using matrix factorization technology to fuse user information and project information together. We first detect the community structure using overlapping community discovery algorithm, and mine the clustering information of user interest preference by a fuzzy clustering algorithm based on the project category information. On the other hand, we use project-category attribution matrix and user-project score matrix to get project comprehensive similarity and compute project feature matrix based on Entity Relation Decomposition. Fusing the user clustering information and project information together, we get Entity-Association-based Matrix Factorization (EAMF) model which can be used to predict user ratings. The proposed algorithm is compared with other algorithms on the Yelp dataset. Experimental studies show that the proposed algorithm leads to a substantial increase in recommendation accuracy on Yelp data set. [ABSTRACT FROM AUTHOR]

Recovering low-rank matrices from incomplete observations is a fundamental problem with many applications, especially in recommender systems. In theory, under certain conditions, this problem can be solved by convex or nonconvex relaxation. However, most existing provable algorithms suffer from superlinear per-iteration cost, which severely limits their applicability to large-scale problems. In this paper, we propose a novel fuzzy double trace norm minimization (DTNM) method for recommender systems. We first present a tractable DTNM model, in which we can integrate both the user social relationship and the user reputation information using a fuzzy weighting way and coupling fuzzy matrix factorization. In essence, our model is a Schatten- ${1/2}$ quasi-norm minimization problem. Moreover, we develop two efficient augmented Lagrangian algorithms to solve the proposed problems, and prove the convergence of our algorithms. Finally, we investigate the empirical recoverability properties of our model and its advantage over classical trace norm. Extensive experimental results on both synthetic and real-world data sets verified both the efficiency and effectiveness of our method compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The mobile service is a widely used carrier for mobile applications. With the increase of the number of mobile services, for service recommendation and selection, the nonfunctional properties (also known as quality of service, QoS) become increasingly important. However, inmany cases, the number ofmobile services invoked by a user is quite limited, which leads to the large number of missing QoS values. In recent years, many prediction algorithms, such as algorithms extended from collaborative filtering (CF), are proposed to predict QoS values. However, the ideas of most existing algorithms are borrowed from the recommender system community, not specific formobile service. In this paper, we first propose a data filtering-extended SlopeOne model (filtering-based CF), which is based on the characteristics of a mobile service and considers the relation with location. Also, using the data filtering technique in FB-CF and matrix factorization (MF), this paper proposes another model FB-MF (filtering-basedMF). We also build an ensemble model, which combines the prediction results of FB-CF model and FB-MF model. We conduct sufficient experiments, and the experimental results demonstrate that ourmodels outperformall comparedmethods and achieve good results in high data sparsity scenario. [ABSTRACT FROM AUTHOR]