A novel collaborative filtering model based on combination of correlation method with matrix completion technique

Bojnordi, Ehsan; Moradi, Parham

doi:10.1109/aisp.2012.6313742

Cited by 22 publications

(13 citation statements)

References 7 publications

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“…They depend largely on the historical rating data of users on items. Most of the time, the rating matrix is always very big and sparse due to the fact that users do not rate most of the items represented within the matrix [61]. This problem always leads to the inability of the system to give reliable and accurate recommendations to users.…”

Section: Model-based Techniquesmentioning

confidence: 96%

Recommendation systems: Principles, methods and evaluation

Isinkaye

Folajimi

Ojokoh

2015

Egyptian Informatics Journal

1,042

437

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On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems.Ó 2015 Production and hosting by Elsevier B.V. on behalf

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Section: Model-based Techniquesmentioning

confidence: 96%

Recommendation systems: Principles, methods and evaluation

Isinkaye

Folajimi

Ojokoh

2015

Egyptian Informatics Journal

1,042

437

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“…In RS, the most frequently used matrix factorization methods are singular value decomposition (SVD) [2,15], latent factor model (LFM) [2,39,41], nonnegative matrix factorization (NMF) [23,[42][43][44][45], and trust-aware matrix factorization (TMF) [19,20,[46][47][48], which are techniques of dimensionality reduction with implementation to RS. Reductions in dimensionality effectively preserve the information content while drastically decreasing the computation complexity and memory requirements for making recommendations [15,39].…”

Section: Matrix Factorizationmentioning

confidence: 99%

A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

Chen

Hua

Gao

et al. 2018

Mathematical Problems in Engineering

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Recommender systems are recently becoming more significant in the age of rapid development of the information technology and pervasive computing to provide e-commerce users’ appropriate items. In recent years, various model-based and neighbor-based approaches have been proposed, which improve the accuracy of recommendation to some extent. However, these approaches are less accurate than expected when users’ ratings on items are very sparse in comparison with the huge number of users and items in the user-item rating matrix. Data sparsity and high dimensionality in recommender systems have negatively affected the performance of recommendation. To solve these problems, we propose a hybrid recommendation approach and framework using Gaussian mixture model and matrix factorization technology. Specifically, the improved cosine similarity formula is first used to get users’ neighbors, and initial ratings on unrated items are predicted. Second, users’ ratings on items are converted into users’ preferences on items’ attributes to reduce the problem of data sparsity. Again, the obtained user-item-attribute preference data is trained through the Gaussian mixture model to classify users with the same interests into the same group. Finally, an enhanced social matrix factorization method fusing user’s and item’s social relationships is proposed to predict the other unseen ratings. Extensive experiments on two real-world datasets are conducted and the results are compared with the existing major recommendation models. Experimental results demonstrate that the proposed method achieves the better performance compared to other techniques in accuracy.

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“…Memorybased methods use a given similarity metric to act directly on the user-item rating matrix, which contains ratings of all of the users who have expressed their preferences on the items [1,9,10,[16][17][18][19]. The similarity metric is used to compute the distance between two users or two items based on their respective ratings.…”

Section: Introductionmentioning

confidence: 99%

“…neighbor users) are used to produce suitable recommendations to a given user (i.e. an active user) in the recommendation process [1,[8][9][10][11][12][13][14][15][16]. The CF approach uses rates of the active user to previously purchased items to make recommendations.…”

Section: Introductionmentioning

confidence: 99%