Analytical Techniques for Describing User Preferences: Justification for (and Extension Of) the Matrix Factorization Technique

2020 ◽  
pp. 5-9
Author(s):  
Griselda Acosta ◽  
Eric Smith ◽  
Vladik Kreinovich
Keyword(s):  
Matrix Factorization ◽  
Analytical Techniques ◽  
User Preferences ◽  
Factorization Technique ◽  
The Matrix ◽  
Matrix Factorization Technique

scholarly journals Detect User’s Rating Characteristics by Separate Scores for Matrix Factorization Technique

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 616 ◽  
Author(s):  
Jia Zhao ◽  
Gang Sun
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Information Overload ◽  
Bias Score ◽  
Factorization Technique ◽  
The Matrix ◽  
Novel Method ◽  
Open Datasets ◽  
Accuracy Of Prediction ◽  
Matrix Factorization Technique

A recommender system can effectively solve the problem of information overload in the era of big data. Recent research on recommender systems, specifically Collaborative Filtering, has focused on Matrix Factorization methods, which have been shown to have excellent performance. However, these methods do not pay attention to the influence of a user’s rating characteristics, which are especially important for the accuracy of prediction or recommendation. Therefore, in order to get better performance, we propose a novel method based on matrix factorization. We consider that the user’s rating score is composed of two parts: the real score, which is decided by the user’s preferences; and the bias score, which is decided by the user’s rating characteristics. We then analyze the user’s historical behavior to find his rating characteristics by using the matrix factorization technique and use them to adjust the final prediction results. Finally, by comparing with the latest algorithms on the open datasets, we verified that the proposed method can significantly improve the accuracy of recommender systems and achieve the best performance in terms of prediction accuracy criterion over other state-of-the-art methods.

scholarly journals Augmenting matrix factorization technique with the combination of tags and genres

2016 ◽  
Vol 461 ◽  
pp. 101-116 ◽  
Author(s):  
Tinghuai Ma ◽  
Xiafei Suo ◽  
Jinjuan Zhou ◽  
Meili Tang ◽  
Donghai Guan ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Factorization Technique ◽  
Matrix Factorization Technique
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