scholarly journals Metric Factorization with Item Cooccurrence for Recommendation

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 512 ◽  
Author(s):  
Honglin Dai ◽  
Liejun Wang ◽  
Jiwei Qin
Keyword(s):  
Matrix Factorization ◽  
Euclidean Distance ◽  
Metric Learning ◽  
Sparse Data ◽  
Inner Product ◽  
Interaction Matrix ◽  
Latent Factors ◽  
Rating Prediction ◽  
Real World Datasets ◽  
Item Item

In modern recommender systems, matrix factorization has been widely used to decompose the user–item matrix into user and item latent factors. However, the inner product in matrix factorization does not satisfy the triangle inequality, and the problem of sparse data is also encountered. In this paper, we propose a novel recommendation model, namely, metric factorization with item cooccurrence for recommendation (MFIC), which uses the Euclidean distance to jointly decompose the user–item interaction matrix and the item–item cooccurrence with shared latent factors. The item cooccurrence matrix is obtained from the colike matrix through the calculation of pointwise mutual information. The main contributions of this paper are as follows: (1) The MFIC model is not only suitable for rating prediction and item ranking, but can also well overcome the problem of sparse data. (2) This model incorporates the item cooccurrence matrix into metric learning so it can better learn the spatial positions of users and items. (3) Extensive experiments on a number of real-world datasets show that the proposed method substantially outperforms the compared algorithm in both rating prediction and item ranking.

scholarly journals Joint Geosequential Preference and Distance Metric Factorization for Point-of-Interest Recommendation

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Chunyang Liu ◽  
Chao Liu ◽  
Haiqiang Xin ◽  
Jian Wang ◽  
Jiping Liu ◽  
...  
Keyword(s):  
Metric Space ◽  
Matrix Factorization ◽  
Euclidean Distance ◽  
Large Scale ◽  
Inner Product ◽  
Interaction Matrix ◽  
Distance Metric ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Real World Datasets

Point-of-interest (POI) recommendation is a valuable service to help users discover attractive locations in location-based social networks (LBSNs). It focuses on capturing users’ movement patterns and location preferences by using massive historical check-in data. In the past decade, matrix factorization has become a mature and widely used technology in POI recommendation. However, the inner product of latent vectors adopted in matrix factorization methods does not satisfy the triangle inequality property, which may limit the expressiveness and lead to suboptimal solutions. Besides, the extreme sparsity of check-in data makes it challenging to capture users’ movement preferences accurately. In this paper, we propose a joint geosequential preference and distance metric factorization framework, called GeoSeDMF, for POI recommendation. First, we introduce a distance metric factorization method that is capable of learning users’ personalized preferences from a position and distance perspective in the metric space. Specifically, we convert the user-POI interaction matrix into a distance matrix and factorize it into user and POI dense embeddings. Additionally, we measure users’ personalized preference for the POI by using the Euclidean distance metric instead of the inner product. Then, we model the users’ geospatial preference by applying a geographic weight coefficient and model the users’ sequential preference by using the Euclidean distance of continuous check-in locations. Moreover, a pointwise loss strategy and AdaGrad algorithm are adopted to optimize the positions and relationships of users and POIs in a metric space. Finally, experimental results on three large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.

Enhancing rating prediction for recommendation by metric learning with gray relational analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiangmei Chen ◽  
Wende Zhang ◽  
Qishan Zhang
Keyword(s):  
Metric Learning ◽  
Prediction Algorithm ◽  
Gray Relational Analysis ◽  
Rating Data ◽  
Data Sparsity ◽  
Content Type ◽  
Relational Analysis ◽  
Rating Prediction ◽  
Real World Datasets ◽  
The Impact

PurposeThe purpose of the paper is to improve the rating prediction accuracy in recommender systems (RSs) by metric learning (ML) method. The similarity metric of user and item is calculated with gray relational analysis.Design/methodology/approachFirst, the potential features of users and items are captured by exploiting ML, such that the rating prediction can be performed. In metric space, the user and item positions can be learned by training their embedding vectors. Second, instead of the traditional distance measurements, the gray relational analysis is employed in the evaluation of the position similarity between user and item, because the latter can reduce the impact of data sparsity and further explore the rating data correlation. On the basis of the above improvements, a new rating prediction algorithm is proposed. Experiments are implemented to validate the effectiveness of the algorithm.FindingsThe novel algorithm is evaluated by the extensive experiments on two real-world datasets. Experimental results demonstrate that the proposed model achieves remarkable performance on the rating prediction task.Practical implicationsThe rating prediction algorithm is adopted to predict the users' preference, and then, it provides personalized recommendations for users. In fact, this method can expand to the field of classification and provide potentials for this domain.Originality/valueThe algorithm can uncover the finer grained preference by ML. Furthermore, the similarity can be measured using gray relational analysis, which can mitigate the limitation of data sparsity.

scholarly journals A Rating Prediction Recommendation Model Combined with the Optimizing Allocation for Information Granularity of Attributes

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 21
Author(s):  
Jianfei Li ◽  
Yongbin Wang ◽  
Zhulin Tao
Keyword(s):  
Neural Network ◽  
Latent Factors ◽  
Latent Factor ◽  
Information Granularity ◽  
Node Attribute ◽  
Item Attribute ◽  
Rating Prediction ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Granularity Distribution

In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for training and learning. A user–item graph, a user–attribute graph, and an item–attribute graph are constructed according to the interactions between users and items. The latent factors of users and items can be learned in these graph structure data. There are many methods for learning the latent factors of users and items. Still, they do not fully consider the influence of node attribute information on the representation of the latent factors of users and items. We propose a rating prediction recommendation model, short for LNNSR, utilizing the level of information granularity allocated on each attribute by developing a granular neural network. The different granularity distribution proportion weights of each attribute can be learned in the granular neural network. The learned granularity allocation proportion weights are integrated into the latent factor representation of users and items. Thus, we can capture user-embedding representations and item-embedding representations more accurately, and it can also provide a reasonable explanation for the recommendation results. Finally, we concatenate the user latent factor-embedding and the item latent factor-embedding and then feed it into a multi-layer perceptron for rating prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.

scholarly journals Optimizing Latent Factors and Collaborative Filtering for Students’ Performance Prediction

2020 ◽  
Vol 10 (16) ◽  
pp. 5601
Author(s):  
Juan A. Gómez-Pulido ◽  
Arturo Durán-Domínguez ◽  
Francisco Pajuelo-Holguera
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Prediction Accuracy ◽  
Search Space ◽  
Learning Rate ◽  
Latent Factors ◽  
Gradient Descent Algorithm ◽  
Real World Datasets ◽  
The Right ◽  
Key Aspects

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.

scholarly journals NeuRec: On Nonlinear Transformation for Personalized Ranking

2018 ◽  
Author(s):  
Shuai Zhang ◽  
Lina Yao ◽  
Aixin Sun ◽  
Sen Wang ◽  
Guodong Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Real World ◽  
Real Life ◽  
Interaction Matrix ◽  
Interaction Patterns ◽  
Latent Factors ◽  
Integrated Network ◽  
Ranking Task ◽  
Real World Datasets ◽  
Personalized Ranking

Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: user-based NeuRec and item-based NeuRec, by focusing on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.

scholarly journals Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation

2019 ◽  
Author(s):  
Yanan Xu ◽  
Yanmin Zhu ◽  
Yanyan Shen ◽  
Jiadi Yu
Keyword(s):  
Sequence Data ◽  
User Preference ◽  
Interaction Matrix ◽  
Interaction Data ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Information User ◽  
Vertex Representation ◽  
Real World Datasets ◽  
Item Item

Collaborative Filtering (CF) is among the most successful techniques in recommendation tasks. Recent works have shown a boost of performance of CF when introducing the pairwise relationships between users and items or among items (users) using interaction data. However, these works usually only utilize one kind of information, i.e., user preference in a user-item interaction matrix or item dependency in interaction sequences which can limit the recommendation performance. In this paper, we propose to mine three kinds of information (user preference, item dependency, and user similarity on behaviors) by converting interaction sequence data into multiple graphs (i.e., a user-item graph, an item-item graph, and a user-subseq graph). We design a novel graph convolutional network (PGCN) to learn shared representations of users and items with the three heterogeneous graphs. In our approach, a neighbor pooling and a convolution operation are designed to aggregate features of neighbors. Extensive experiments on two real-world datasets demonstrate that our graph convolution approaches outperform various competitive methods in terms of two metrics, and the heterogeneous graphs are proved effective for improving recommendation performance.

Collaboration Matrix Factorization on Rate and Review for Recommendation

2019 ◽  
Vol 30 (2) ◽  
pp. 27-43
Author(s):  
Zhicheng Wu ◽  
Huafeng Liu ◽  
Yanyan Xu ◽  
Liping Jing
Keyword(s):  
Neural Networks ◽  
Recommender Systems ◽  
Projection Method ◽  
Matrix Factorization ◽  
Auxiliary Information ◽  
Latent Factors ◽  
Latent Factor ◽  
Real World Datasets ◽  
Rating Information

According to the sparseness of rating information, the quality of recommender systems has been greatly restricted. In order to solve this problem, much auxiliary information has been used, such as social networks, review information, and item description. Convolutional neural networks (CNNs) have been widely employed by recommender systems, it greatly improved the rating prediction's accuracy especially when combined with traditional recommendation methods. However, a large amount of research focuses on the consistency between the rating-based latent factor and review-based latent factor. But in fact, these two parts are completely different. In this article, the authors propose a model named collaboration matrix factorization (CMF) that combines a projection method with a convolutional matrix factorization (ConvMF) to extract the collaboration between rating-based latent factors and review-based latent factors that comes from the results of the CNN process. Extensive experiments on three real-world datasets show that the projection method achieves significant improvements over the existing baseline.

Factor-Bounded Nonnegative Matrix Factorization

2021 ◽  
Vol 15 (6) ◽  
pp. 1-18
Author(s):  
Kai Liu ◽  
Xiangyu Li ◽  
Zhihui Zhu ◽  
Lodewijk Brand ◽  
Hua Wang
Keyword(s):  
Matrix Factorization ◽  
Clustering Algorithm ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Optimization Methods ◽  
Auxiliary Function ◽  
Image Clustering ◽  
Real World Datasets ◽  
The Relationship ◽  
Matrix Factors

Nonnegative Matrix Factorization (NMF) is broadly used to determine class membership in a variety of clustering applications. From movie recommendations and image clustering to visual feature extractions, NMF has applications to solve a large number of knowledge discovery and data mining problems. Traditional optimization methods, such as the Multiplicative Updating Algorithm (MUA), solves the NMF problem by utilizing an auxiliary function to ensure that the objective monotonically decreases. Although the objective in MUA converges, there exists no proof to show that the learned matrix factors converge as well. Without this rigorous analysis, the clustering performance and stability of the NMF algorithms cannot be guaranteed. To address this knowledge gap, in this article, we study the factor-bounded NMF problem and provide a solution algorithm with proven convergence by rigorous mathematical analysis, which ensures that both the objective and matrix factors converge. In addition, we show the relationship between MUA and our solution followed by an analysis of the convergence of MUA. Experiments on both toy data and real-world datasets validate the correctness of our proposed method and its utility as an effective clustering algorithm.

scholarly journals Chi-Squared Distance Metric Learning for Histogram Data

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Yang ◽  
Luhui Xu ◽  
Xiaopan Chen ◽  
Fengbin Zheng ◽  
Yang Liu
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Metric Learning ◽  
Nearest Neighbors ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Projected Gradient Method ◽  
Proper Distance ◽  
Chi Squared ◽  
Real World Datasets

Learning a proper distance metric for histogram data plays a crucial role in many computer vision tasks. The chi-squared distance is a nonlinear metric and is widely used to compare histograms. In this paper, we show how to learn a general form of chi-squared distance based on the nearest neighbor model. In our method, the margin of sample is first defined with respect to the nearest hits (nearest neighbors from the same class) and the nearest misses (nearest neighbors from the different classes), and then the simplex-preserving linear transformation is trained by maximizing the margin while minimizing the distance between each sample and its nearest hits. With the iterative projected gradient method for optimization, we naturally introduce thel2,1norm regularization into the proposed method for sparse metric learning. Comparative studies with the state-of-the-art approaches on five real-world datasets verify the effectiveness of the proposed method.

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