Incorporating Hierarchical Information into the Matrix Factorization Model for Collaborative Filtering

2012 ◽  
pp. 504-513 ◽  
Author(s):  
Ali Mashhoori ◽  
Sattar Hashemi
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Factorization Model ◽  
The Matrix

scholarly journals Analisis Perbandingan Model Matrix Factorization dan K-Nearest Neighbor dalam Mesin Rekomendasi Collaborative Berbasis Prediksi Rating

2021 ◽  
Vol 5 (4) ◽  
pp. 506
Author(s):  
Janny Eka Prayogo ◽  
Aries Suharso ◽  
Adhi Rizal
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Absolute Error ◽  
K Nearest Neighbor ◽  
Filtering Method ◽  
Factorization Model ◽  
The Matrix

Rating is a form of assessment of the likes or dislikes of a user or customer for an item. Where the higher the rating number given, the item is preferred by customers or users. In the recommendation engine, a set of ratings can be predicted and used as an object to generate a recommendation by the Collaborative Filtering method. In the Collaborative Filtering method, there is a rating prediction model, namely the Matrix Factorization and K-Nearest Neighbor models. This study analyzes the comparison of the two prediction models based on the value of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the prediction results generated using the movielens film rating dataset. From the analysis and testing results, it was found that MAE = 0.6371 and RMSE = 0.8305 for the Matrix Factorization model, while MAE = 0.6742 and RMSE = 0.8863 for the K-Nearest Neighbor model. The best model is Matrix Factorization because the MAE and RMSE values are lower than the K-Nearest Neighbor model and have the closest predicted rating results from the original rating value.

scholarly journals Collaborative Filtering Recommendation Using Nonnegative Matrix Factorization in GPU-Accelerated Spark Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bing Tang ◽  
Linyao Kang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Collaborative Filtering ◽  
Processing Speed ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Experimental Results ◽  
Computational Time ◽  
Data Sets ◽  
Heterogeneous Cluster ◽  
The Matrix

Nonnegative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data compression and its capability of extracting highly interpretable parts from data sets, and it has also been applied to various fields, such as recommendations, image analysis, and text clustering. However, as the size of the matrix increases, the processing speed of nonnegative matrix factorization is very slow. To solve this problem, this paper proposes a parallel algorithm based on GPU for NMF in Spark platform, which makes full use of the advantages of in-memory computation mode and GPU acceleration. The new GPU-accelerated NMF on Spark platform is evaluated in a 4-node Spark heterogeneous cluster using Google Compute Engine by configuring each node a NVIDIA K80 CUDA device, and experimental results indicate that it is competitive in terms of computational time against the existing solutions on a variety of matrix orders. Furthermore, a GPU-accelerated NMF-based parallel collaborative filtering (CF) algorithm is also proposed, utilizing the advantages of data dimensionality reduction and feature extraction of NMF, as well as the multicore parallel computing mode of CUDA. Using real MovieLens data sets, experimental results have shown that the parallelization of NMF-based collaborative filtering on Spark platform effectively outperforms traditional user-based and item-based CF with a higher processing speed and higher recommendation accuracy.

scholarly journals Followee Recommendation in Microblog Using Matrix Factorization Model with Structural Regularization

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yan Yu ◽  
Robin G. Qiu
Keyword(s):  
Social Network ◽  
Matrix Factorization ◽  
Information Sources ◽  
Quality Information ◽  
High Quality ◽  
Structure Information ◽  
Factorization Model ◽  
Proposed Model ◽  
The Matrix ◽  
High Quality Information

Microblog that provides us a new communication and information sharing platform has been growing exponentially since it emerged just a few years ago. To microblog users, recommending followees who can serve as high quality information sources is a competitive service. To address this problem, in this paper we propose a matrix factorization model with structural regularization to improve the accuracy of followee recommendation in microblog. More specifically, we adapt the matrix factorization model in traditional item recommender systems to followee recommendation in microblog and use structural regularization to exploit structure information of social network to constrain matrix factorization model. The experimental analysis on a real-world dataset shows that our proposed model is promising.

scholarly journals Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey

2015 ◽  
Vol 49 ◽  
pp. 136-146 ◽  
Author(s):  
Dheeraj Bokde ◽  
Sheetal Girase ◽  
Debajyoti Mukhopadhyay
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Filtering Algorithms ◽  
Factorization Model

scholarly journals Building Recommendation Systems Using the Algorithms KNN and SVD

2021 ◽  
Vol 9 (1) ◽  
pp. 71
Author(s):  
Mohammed Erritali ◽  
Badr Hssina ◽  
Abdelkader Grota
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Singular Value ◽  
User Preferences ◽  
Factorization Algorithm ◽  
The Matrix ◽  
Filtering Approach

<p>Recommendation systems are used successfully to provide items (example:<br />movies, music, books, news, images) tailored to user preferences.<br />Among the approaches proposed, we use the collaborative filtering approach<br />of finding the information that satisfies the user by using the<br />reviews of other users. These ratings are stored in matrices that their<br />sizes increase exponentially to predict whether an item is interesting<br />or not. The problem is that these systems overlook that an assessment<br />may have been influenced by other factors which we call the cold start<br />factor. Our objective is to apply a hybrid approach of recommendation<br />systems to improve the quality of the recommendation. The advantage<br />of this approach is the fact that it does not require a new algorithm<br />for calculating the predictions. We we are going to apply the two Kclosest<br />neighbor algorithms and the matrix factorization algorithm of<br />collaborative filtering which are based on the method of (singular value<br />decomposition).</p>

Attribute interaction aware matrix factorization method for recommendation

2021 ◽  
Vol 25 (5) ◽  
pp. 1115-1130
Author(s):  
Yongquan Wan ◽  
Lihua Zhu ◽  
Cairong Yan ◽  
Bofeng Zhang
Keyword(s):  
Parallel Computing ◽  
Matrix Factorization ◽  
Time Complexity ◽  
Optimization Strategy ◽  
Computing Technology ◽  
Training Time ◽  
Latent Vector ◽  
Factorization Model ◽  
The Matrix ◽  
Original Rating

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.

scholarly journals Matrix Factorization-Based Prediction of Novel Drug Indications by Integrating Genomic Space

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wen Dai ◽  
Xi Liu ◽  
Yibo Gao ◽  
Lin Chen ◽  
Jianglong Song ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
New Drugs ◽  
Biological Information ◽  
Gene Interactions ◽  
Computational Techniques ◽  
Interaction Data ◽  
Factorization Model ◽  
The Matrix ◽  
Disease Associations ◽  
Novel Drug

There has been rising interest in the discovery of novel drug indications because of high costs in introducing new drugs. Many computational techniques have been proposed to detect potential drug-disease associations based on the creation of explicit profiles of drugs and diseases, while seldom research takes advantage of the immense accumulation of interaction data. In this work, we propose a matrix factorization model based on known drug-disease associations to predict novel drug indications. In addition, genomic space is also integrated into our framework. The introduction of genomic space, which includes drug-gene interactions, disease-gene interactions, and gene-gene interactions, is aimed at providing molecular biological information for prediction of drug-disease associations. The rationality lies in our belief that association between drug and disease has its evidence in the interactome network of genes. Experiments show that the integration of genomic space is indeed effective. Drugs, diseases, and genes are described with feature vectors of the same dimension, which are retrieved from the interaction data. Then a matrix factorization model is set up to quantify the association between drugs and diseases. Finally, we use the matrix factorization model to predict novel indications for drugs.

scholarly journals TrustRec: An effective approach to exploit implicit trust and distrust relationships along with explicitones for accurate recommendations

2020 ◽  
pp. 39-39
Author(s):  
Reyhani Hamedani ◽  
Irfan Ali ◽  
Jiwon Hong ◽  
Sang-Wook Kim
Keyword(s):  
Matrix Factorization ◽  
Computational Cost ◽  
Effective Solution ◽  
Trust Networks ◽  
Novel Approach ◽  
Factorization Model ◽  
Implicit Trust ◽  
The Matrix ◽  
Effectiveness And Efficiency ◽  
Single Matrix

Trust-aware recommendation approaches are widely used to mitigate the cold-start problem in recommender systems by utilizing trust networks. In this paper, we point out the problems of existing trust-aware recommendation approaches as follows: (P1) exploiting sparse explicit trust and distrust relationships; (P2) considering a misleading assumption that a user pair having a trust/distrust relationship certainly has a similar/dissimilar preference in practice; (P3) employing the transitivity of distrust relationships. Then, we propose TrustRec, a novel approach based on the matrix factorization that provides an effective solution to each of the afore mentioned problems and incorporates all of them in a single matrix factorization model. Furthermore, TrustRec exploits only top-k most similar trustees and dissimilar distrustees of each user to improve both the computational cost and accuracy. The results of our extensive experiments demonstrate that TructRec outperforms existing approaches in terms of both effectiveness and efficiency.

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