Matrix Factorization Recommendation Algorithm Research Articles

Matrix Factorization Recommendation Algorithm Based on Attention Interaction

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.

Improved ALS Recommendation Algorithm Based on Interest-Value Model

Abstract In the era of big data, all kinds of information around, how to effectively get the information others want and achieve recommendation is the original purpose of recommendation algorithm, traditional recommendation has collaborative filtering, content-based, model-based recommendation algorithm. Matrix factorization recommendation algorithm solves the problems of data sparsity and low space utilization brought by dimensional explosion. ALS(Alternating Least Squares) algorithm is one of the matrix factorization and the only one recommendation algorithm implemented in Spark, which is in line with the computation method of big data distributed computing. However, in the traditional ALS, there are still some shortcomings: the determination of the number of Implicit features does not give a clear definition, the information factor is lost after matrix factorization, which leads to the poor interpretability of the decomposed matrix, and the performance parameters of the ALS model are not optimal in terms of performance. Based on the above shortcomings, the IVM-ALS(ALS Based on Interest-Value Model) based on interest-value is proposed, an interest-value model is introduced, a solution to solve the Implicit feature number is introduced, and the feasibility of the feature matrix of the subdivided item is enhanced, so as to meet the actual requirements., And improve the algorithm performance than traditional ALS.

Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback.

Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear feature of explicit-implicit feedback of user-item interaction. Second, combined with the traditional matrix factorization, explicit feedback is used to accurately reflect the explicit preference and the potential preferences of users to build a recommendation model; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according to prediction results, personalized recommendation list is pushed to the user. The feasibility, validity, and robustness are fully demonstrated in comparison with multiple baseline models on two real datasets.

Matrix Factorization Recommendation Algorithm Based on Multiple Social Relationships

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.

Hybrid matrix factorization recommendation algorithm based on item similarity

In order to solve the problem of sparse data in matrix factorization recommendation algorithm, a hybrid matrix factorization recommendation algorithm based on item similarity is proposed. First of all, Worde2vec is used to mine the content information of items in user’s comment data to get the content similarity of items; secondly, the attribute similarity of items is calculated according to the attribute information of items; then, the content similarity and attribute similarity are fused to get the final item similarity; finally, the similarity of item is integrated into the objective function of matrix factorization algorithm. The experimental results show that the hybrid recommendation algorithm has higher accuracy, recall and coverage than the traditional matrix factorization recommendation algorithm.

Distributed matrix factorization based on fast optimization for implicit feedback recommendation

In big data scenarios, matrix factorization (MF) is widely used in recommendation systems as it can offer high accuracy and scalability. However, when using MF to process large-scale implicit feedback data, the following two problems arise. One is that it is difficult to effectively obtain negative feedback information, which causes relatively poor recommendation accuracy. The other is that the limited resources of a single machine make the model training inefficient, and in particular, the acquisition of negative feedback information further increases the time complexity of model training. In order to solve the above two problems, we first propose a user-activity and item-popularity weighted matrix factorization (UIWMF) recommendation algorithm, which assigns every missing data different weight based on user activity and item popularity, gets negative feedback information more realistically, and leads to better recommendation accuracy. Meanwhile, in order to reduce the additional computational overhead caused by the weight strategy, we develop a fast optimization strategy to enhance the efficiency. In order to break the resource constraints of a single machine, we propose a distributed UIWMF (DUIWMF) algorithm based on Spark, which adopts an efficient parallel learning algorithm to train the model and utilizes cached in-block and out-block information to effectively reduce the communication overhead in a distributed environment. We conduct experiments on three public datasets, and the experimental results demonstrate that, comparing with the baseline MF methods, DUIWMF model has comparable performance in terms of recommendation accuracy and model training efficiency.

Matrix factorization recommendation algorithms based on knowledge map representation learning1

The matrix factorization recommendation algorithm does not consider characteristics of the recommendation object itself, resulting in poor recommendation results. Therefore, a matrix factorization recommendation algorithm based on knowledge map representation learning is proposed. Firstly, the recommendation object is represented as a low dimensional semantic vector by using the knowledge map distributed representation learning algorithm. Then the semantic similarity between objects is calculated, and the semantic similarity is incorporated into the objective optimization function of matrix factorization, so that the feature vectors obtained by matrix factorization can also contain semantic knowledge, which makes up for the shortcoming of matrix factorization recommendation algorithm that does not consider characteristics of the recommendation object itself from the semantic perspective. The experimental results show that the improved algorithm has higher accuracy, recall and coverage than the traditional matrix factorization recommendation algorithm.

A social trust and preference segmentation-based matrix factorization recommendation algorithm

A recommendation can inspire potential demands of users and make e-commerce platforms more intelligent and is essential for e-commerce enterprises’ sustainable development. The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. To solve these problems mentioned above, we propose a social trust and preference segmentation-based matrix factorization (SPMF) recommendation algorithm. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly superior to that of some state-of-the-art recommendation algorithms. The SPMF algorithm is a better recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing.

Personalization Recommendation Algorithm Based on Trust Correlation Degree and Matrix Factorization

The rapid development of the Internet of Things (IoT) and e-commerce has brought a lot of convenience to people’s lives. IoT applications generate a large number of services and user data. It is necessary to design a personalized recommendation technology suitable for the users of IoT services and improve the user experience. In this paper, a recommendation algorithm with trusted relevance combined with matrix factorization is proposed. By establishing an effective trust metric model, the user’s social information is integrated into the recommendation algorithm. First, the social network concentric hierarchical model is used to consider the direct or indirect trust relationship, and more trust information is integrated for the matrix factorization recommendation algorithm. Then, we design the trust relevance, comprehensively considering the trust factors and interest similar factors. Our experiments were performed on the Dianping datasets. The recommendation algorithm using matrix factorization and trusted relevance degree has higher prediction accuracy than the basic matrix decomposition and social matrix factorization in terms of accuracy and stability.

Double Regularization Matrix Factorization Recommendation Algorithm

With the development of social networks, the research of integrated social information recommendation models has received extensive attention. However, most existing social recommendation models are based on the matrix factorization technique which ignore the impact of the relationships between items on users’ interests, resulting in a decline of recommendation accuracy. To solve this problem, this paper proposes a double regularization matrix factorization recommendation algorithm. The algorithm first uses attribute information and manifold learning to calculate similarity. Then, the matrix factorization model is constrained through the regularization of item association relations and user social relations. Experimental results on real datasets show that the proposed method can effectively alleviate problems such as cold start and data sparsity in the recommender system and improve the recommendation accuracy compared with those of existing methods.

Matrix Factorization Based Recommendation System using Hybrid Optimization Technique

In this paper, a matrix factorization recommendation algorithm is used to recommend items to the user by inculcating a hybrid optimization technique that combines Alternating Least Squares (ALS) and Stochastic Gradient Descent (SGD) in the advanced stage and compares the two indiv

Probabilistic Matrix Factorization Recommendation Algorithm with User Trust Similarity

In this paper, we describe the formatting guidelines for Conference Proceedings. Whether the user similarity calculation is reasonable in the traditional collaborative filtering recommendation algorithm directly affects the result of the collaborative filtering recommendation algorithm. This paper proposes a probabilistic matrix factorization recommendation algorithm with user trust similarity which combines improved similarity of users’ trust and probability matrix factorization recommendation method. The results show that proposed algorithm could relieve user cold start issues and effectively reduce the error of recommendation.

Joint user knowledge and matrix factorization for recommender systems

Currently, most of the existing recommendation methods treat social network users equally, which assume that the effect of recommendation on a user is decided by the user’s own preferences and social influence. However, a user’s own knowledge in a field has not been considered. In other words, to what extent does a user accept recommendations in social networks need to consider the user’s own knowledge or expertise in the field. In this paper, we propose a novel matrix factorization recommendation algorithm based on integrating social network information such as trust relationships, rating information of users and users’ own knowledge. Specifically, since we cannot directly measure a user’s knowledge in the field, we first use a user’s status in a social network to indicate a user’s knowledge in a field, and users’ status is inferred from the distributions of users’ ratings and followers across fields or the structure of domain-specific social network. Then, we model the final rating of decision-making as a linear combination of the user’s own preferences, social influence and user’s own knowledge. Experimental results on real world data sets show that our proposed approach generally outperforms the state-of-the-art recommendation algorithms that do not consider the knowledge level difference between the users.

Research on Recommendation Algorithm of Matrix Factorization Method Based on MapReduce

Matrix factorization is a collaborative filtering recommendation technique proposed in recent years.In the process of recommendation,each prediction depends on the collaboration of the whole known rating set and the feature matrices need huge storage. So the recommendation with only one node will meet the bottleneck of time and resource. A MapReduce-based matrix factorization recommendation algorithm was proposed to solve this problem.The big feature matrices were shared by Hadoop distributed cache and MapFile techniques.The MapReduce algorithm could also handle multi-λsituation.The experiment on Netflix data set shows that the MapReduce-based algorithm has high speedup and improves the efficiency of collaborative filtering.