A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model

2016 ◽  
Vol 97 ◽  
pp. 188-202 ◽  
Author(s):  
Antonio Hernando ◽  
Jesús Bobadilla ◽  
Fernando Ortega
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Probabilistic Model ◽  
Bayesian Probabilistic Model ◽  
Non Negative Matrix Factorization

Mining intrinsic information by matrix factorization-based approaches for collaborative filtering in recommender systems

2017 ◽  
Vol 249 ◽  
pp. 48-63 ◽  
Author(s):  
Yangyang Li ◽  
Dong Wang ◽  
Haiyang He ◽  
Licheng Jiao ◽  
Yu Xue
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization

scholarly journals Recommender Systems Clustering Using Bayesian Non Negative Matrix Factorization

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 3549-3564 ◽  
Author(s):  
Jesus Bobadilla ◽  
Rodolfo Bojorque ◽  
Antonio Hernando Esteban ◽  
Remigio Hurtado
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Non Negative Matrix Factorization

scholarly journals Discrete Trust-aware Matrix Factorization for Fast Recommendation

2019 ◽  
Author(s):  
Guibing Guo ◽  
Enneng Yang ◽  
Li Shen ◽  
Xiaochun Yang ◽  
Xiaodong He
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relations ◽  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Proposed Model ◽  
Hamming Space ◽  
Real World Datasets

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

scholarly journals Book Recommendation System using Matrix Factorization

2021 ◽  
Vol 9 (VI) ◽  
pp. 4578-4582
Author(s):  
K. Venkata Ruchitha
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Previous History ◽  
Speed Up ◽  
Area Unit ◽  
Content Based Filtering

In recent years, recommender systems became more and more common and area unit applied to a various vary of applications, thanks to development of things and its numerous varieties accessible, that leaves the users to settle on from bumper provided choices. Recommendations generally speed up searches and create it easier for users to access content that they're curious about, and conjointly surprise them with offers they'd haven't sought for. By victimisation filtering strategies for pre-processing the information, recommendations area unit provided either through collaborative filtering or through content-based Filtering. This recommender system recommends books supported the description and features. It identifies the similarity between the books supported its description. It conjointly considers the user previous history so as to advocate the identical book.

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