Normalized Matrix Factorization with Implicit FeedBack and Baseline Predictor

Joint latent factors and attributes to discover interpretable preferences in recommendation

In the paper, the influence of user trust relationship on recommendation is mainly analyzed, user rating and trust relationship are also clearly defined.Meanwhile, the traditional SVD matrix factorization algorithm is extended to introduce the explicit influence (user trust value) and the implicit influence (friends trusted by users) of the user trust into the matrix factorization recommendation model.Subsequently, TFMF (Trust-fused Matrix Factorization) recommendation model is proposed in the paper to infer model training & learning in detail.The user trust relationship is not considered in implicit feedback SVD++, in other words, the itemratings marked by the trusted users can also influence the recommendation prediction.Therefore, the matrix factorization recommendation algorithm based on implicit trust relationship fusion is designed in the paper.Compared with original matrix factorization algorithm, the proposed algorithm not only has better prediction accuracy and flexibility, but also has better performance in the sparse data set due to the introduction of the trust relationship implicit feedback mechanism.

A multi-attention deep neural network model base on embedding and matrix factorization for recommendation

An important problem of matrix completion/approximation based on Matrix Factorization (MF) algorithms is the existence of multiple global optima; this problem is especially serious when the matrix is sparse, which is common in real-world applications such as personalized recommender systems. In this work, we clarify data sparsity by bounding the solution space of MF algorithms. We present the conditions that an MF algorithm should satisfy for reliable completion of the unobservables, and we further propose to augment current MF algorithms with extra constraints constructed by compressive sampling on the unobserved values, which is well-motivated by the theoretical analysis. Model learning and optimal solution searching is conducted in a properly reduced solution space to achieve more accurate and efficient rating prediction performances. We implemented the proposed algorithms in the Map-Reduce framework, and comprehensive experimental results on Yelp and Dianping datasets verified the effectiveness and efficiency of the augmented matrix factorization algorithms.

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.

INTRODUCTION: Collaborative filtering (CF) algorithms based on deep learning and matrix factorization aim to learn deeper latent features of users and items from user ratings. However, such methods often face challenges due to the sparsity of rating data, which limits their recommendation performance. OBJECTIVES: This study aims to design a hybrid recommendation algorithm named auto-encoder deep learning and matrix factorization (AED-MF), which integrates deep learning and matrix factorization to address the sparsity issue in rating data and improve the extraction of deeper latent features of users and items. METHODS: The AED-MF algorithm combines an auto-encoder-based deep learning approach with matrix factorization to model both deeper hidden representations and nonlinear relationships between users and items. The methodology includes data downloading, mounting, cleaning, model training, and experimental evaluation, with matrix factorization applied to predict missing ratings in the rating matrix. RESULTS: The proposed AED-MF algorithm demonstrated strong performance in key recommendation metrics, effectively learning deeper user and item representations and applying them to capture complex nonlinear relationships. It also reduced the impact of rating data sparsity while maintaining high recommendation accuracy. CONCLUSION: The AED-MF recommendation algorithm successfully alleviates the problem of sparse rating data and enhances recommendation accuracy by leveraging the strengths of both deep learning and matrix factorization, offering an effective solution for improving collaborative filtering-based recommender systems.

In this era of exponential growth in the scale of data, information overload has become an urgent problem, and the use of increasingly flexible sensor cloud systems (SCS) for data collection has become a mainstream trend. Recommendation algorithms can search massive data sets to uncover information that meets the needs of users based on their interests. To improve the accuracy of recommendation scoring, this article proposes a score prediction algorithm that combines deep learning and matrix factorization. To address the problem of sparse scoring data, our study employs a sensor cloud system to collect data information, preprocesses the collected information, and then uses a deep learning model combined with explicit and implicit feedback to generate recommendations. The proposed algorithm, MF-NeuRec, combines fusion matrix decomposition and the NeuRec model score prediction algorithm. The algorithm employs user-based and item-based NeuRec algorithms to extract the feature vectors of users and items under implicit feedback data. The obtained user and item feature vectors are integrated in a certain ratio through the use of matrix decomposition under the display feedback data. The user and item feature vectors obtained by the algorithm are merged and analyzed to predict how users will rate items. Experiments demonstrate that the algorithm can improve the accuracy of recommendations.

Matrix Factorization (MF) is a well-known method which is to predict users’ preference in recommender systems. This method has the advantage of reducing data sparsity and cold start problem. Probabilistic matrix factorization (PMF) is an improved method of matrix factorization. Recommender system can efficiently reduce the information overload problem. However, it is bound by the recommended accuracy. In this paper we introduce Bayesian distribution to improve accuracy and design our novel probabilistic matrix factorization (NPMF) proved to have better performance. In addition, we also use K nearest neighbor method and stochastic gradient descent to further improve our method. The experimental studies using two different datasets including MovieLens 100k and MovieLens 1M, show that the proposed methods already have comparable and better performance.

In this paper, we describe scalable parallel algorithms for symmetric sparse matrix factorization, analyze their performance and scalability, and present experimental results for up to 1,024 processors on a Gray T3D parallel computer. Through our analysis and experimental results, we demonstrate that our algorithms substantially improve the state of the art in parallel direct solution of sparse linear systems-both in terms of scalability and overall performance. It is a well known fact that dense matrix factorization scales well and can be implemented efficiently on parallel computers. In this paper, we present the first algorithms to factor a wide class of sparse matrices (including those arising from two- and three-dimensional finite element problems) that are asymptotically as scalable as dense matrix factorization algorithms on a variety of parallel architectures. Our algorithms incur less communication overhead and are more scalable than any previously known parallel formulation of sparse matrix factorization. Although, in this paper, we discuss Cholesky factorization of symmetric positive definite matrices, the algorithms can be adapted for solving sparse linear least squares problems and for Gaussian elimination of diagonally dominant matrices that are almost symmetric in structure. An implementation of one of our sparse Cholesky factorization algorithms delivers up to 20 GFlops on a Gray T3D for medium-size structural engineering and linear programming problems. To the best of our knowledge, this is the highest performance ever obtained for sparse Cholesky factorization on any supercomputer.

To enhance students’ English writing ability, English creative writing has become a common course offered by colleges and universities. English reference is an indispensable tool for creative English writing. How to choose appropriate English references is an important guarantee to complete good work. Therefore, this paper proposes a Deep Semantic Mining-based Recommendation (DSMR) algorithm for English writing reference selection and recommendation to assist the completion of high-quality English creative writing works. The model can extract user features and document attributes more accurately by deeply mining semantic information of literature content and user needs, so as to achieve more accurate recommendations. First, the Bidirectional Encoder Representation from Transformers (BERT) pretraining model is adopted to process literature content and user requirement documents. Through in-depth mining of user characteristics and literature attributes, the problems of data sparsity and cold start of items are effectively alleviated. Then, the forward long short-term memory (LSTM) network was used to focus on the changes in user preferences over time, resulting in more accurate recommendations. The experimental results show that the use of heterogeneous information can significantly improve the recommendation performance, and the additional use of user attribute information can also improve the recommendation performance. Compared with other benchmark models, the recommendation quality of this model is greatly improved.

Recommender systems play an important role in most modern e-commerce applications. They have allowed users to become aware of the myriad choices available to them. The ease of information and the abundance of options have helped users make educated decisions. A recommender system studies a user's preferences and continues learning the user's changing interests, so as to suggest items that incline with the user's interests. In cases where a user is new to the application, or the user prefers not to discourse preferences, the recommender system is unable to gather the user's preference on any item. This is called the cold start problem; wherein the system can make valid recommendations only once the user starts informing the system about his/her choices. In this paper, we discuss the challenges faced by the cold start problem and how this problem may be alleviated using social media. We suggest an approach where we collect public information from users' social media accounts and analyze this information to understand their preferences. In particular, we gather the new user's information using their Twitter profile; i.e., the user's interest and preferences are extracted from his/her Twitter profile by analyzing his/her tweets. These interests will help the system understand what kind of movies the user will be most interested in. We compare these preferences with the metadata about the individual items. Using this approach, we develop a movie recommendation system wherein we produce top-N movie recommendations for a user. We used the MovieTweetings dataset to model the application. Two sets of results have been produced. In the first, smaller set of 770 users, 72.67% of users have received 100% accurate movie recommendations while nearly 80% of users got more than 75% accuracy. For the second, larger set of more than 3,500 users, 53 % of users have received 100% accurate recommendations while 72% of users got more than 75% accuracy. These encouraging results have demonstrated that the approach is effectively in alleviating cold start problems in recommendation systems, and may be applicable to many other e-commerce applications.

As to the problem of Cold-Start and Simplification, a new recommendation algorithm named Diversified Recommendation Algorithm for Hybrid Label based on Matrix Factorization (DRA-HLMF) is proposed in this paper. Firstly, the existing preference of users and relevance of items are excavated in the social networks in order to increase the diversity of label recommendations horizontally and vertically(also users and items); Secondly, the trust value by combining the initial label value with the label popularity can ensure the accuracy of label recommendation. Finally, the recommendation is dynamically updated by the time-weight value. Then the recommendation results are obtained by matrix factorization algorithm. Experiments on real data show that the proposed method can improve the coverage and diversity of label recommendation on the basis of guaranteeing accuracy in comparison with classical algorithms.

The main task of recommender systems is to recommend items or any other information which the particular user is interested in accurately and efficiently. However, traditional recommender systems usually suffer from the data sparsity problem and cold start problem. To alleviate these problems, social recommender systems usually combine the social network data and rating data together to improve the accuracy of prediction results. In this paper, we propose SimilarMF, a novel and accurate social recommender system trying to improve the performance by means of utilizing the implicit similarity information among users and items. Firstly, SimilarMF integrates social network with rating data to construct a user similarity graph and an item similarity graph. Secondly, it adopts an embedding method to acquire enough similar user-user pairs and item-item pairs. It also performs negative sampling for dissimilar pairs. Lastly, it incorporates the extracted information into matrix factorization framework to predict ratings more accurately. The experimental results show that our method offers better recommendations than its competitors.

Delayed evolutionary game clustering-based recommendation algorithm via latent information and user preference

The goal of the recommender system is to understand the user's preferences and make appropriate recommendations for certain behaviors. Similarity is an important part of recommendation algorithm. However, most current recommendation algorithms ignore the social relation on users, and the most similarity calculations are based on the common object, which does not fully exploit the implicit information. For these challenges, firstly, an adaptive approach which takes both trust and rating into account is proposed to measure the implicit similarity between a user and his friends when the user as a truster and a trustee, respectively. Secondly, a recommendation algorithm combining trust and ratings is proposed, which also takes the biases of users and items, and the user's implicit feedback into account to enhance the accuracy of the algorithm. Finally, the social regularization recommendation algorithm is modified. The experimental results on two different datasets show that the proposed algorithm is able to not only outperform the traditional recommendation algorithm, but also effectively alleviate the data sparsity and cold start problems.