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 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.

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

scholarly journals Kernelized Matrix Factorization for Collaborative Filtering

2016 ◽  
Author(s):  
Xinyue Liu ◽  
Chara Aggarwal ◽  
Yu-Feng Li ◽  
Xiaugnan Kong ◽  
Xinyuan Sun ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization
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