Detecting Anomalous Ratings Using Matrix Factorization for Recommender Systems

2016 ◽  
pp. 3-14
Author(s):  
Zhihai Yang ◽  
Zhongmin Cai ◽  
Xinyuan Chen
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization

BaPa: A Novel Approach of Improving Load Balance in Parallel Matrix Factorization for Recommender Systems

2020 ◽  
pp. 1-1
Author(s):  
Ruixin Guo ◽  
Feng Zhang ◽  
Lizhe Wang ◽  
Wusheng Zhang ◽  
Xinya Lei ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Load Balance ◽  
Novel Approach

scholarly journals Introduction to Matrix Factorization for Recommender Systems

2021 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Singular Value Decomposition ◽  
Online Education ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Gradient Descent ◽  
Large Scale ◽  
Singular Value ◽  
Factorization Algorithms ◽  
Value Decomposition ◽  
Interaction History

Recommender systems aim to personalize the experience of user by suggesting items to the user based on the preferences of a user. The preferences are learned from the user’s interaction history or through explicit ratings that the user has given to the items. The system could be part of a retail website, an online bookstore, a movie rental service or an online education portal and so on. In this paper, I will focus on matrix factorization algorithms as applied to recommender systems and discuss the singular value decomposition, gradient descent-based matrix factorization and parallelizing matrix factorization for large scale applications.

scholarly journals Deep Matrix Factorization for Recommender Systems with Missing Data not at Random

2018 ◽  
Vol 1060 ◽  
pp. 012001 ◽  
Author(s):  
Fei Zhang ◽  
Jiaxing Song ◽  
Shiyu Peng
Keyword(s):  
Missing Data ◽  
Recommender Systems ◽  
Matrix Factorization

Counteracting the filter bubble in recommender systems: Novelty-aware matrix factorization

2019 ◽  
Vol 13 (1) ◽  
pp. 37-47
Author(s):  
Panagiotis Symeonidis ◽  
Ludovik Coba ◽  
Markus Zanker
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Filter Bubble

Content-Enhanced Matrix Factorization for Recommender Systems

2013 ◽  
Vol 475-476 ◽  
pp. 1084-1089
Author(s):  
Hui Yuan Chang ◽  
Ding Xia Li ◽  
Qi Dong Liu ◽  
Rong Jing Hu ◽  
Rui Sheng Zhang
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Factor Models ◽  
User Preferences ◽  
Latent Factor ◽  
Successful Approach ◽  
Latent Factor Models ◽  
User Ratings ◽  
Important Measurement ◽  
Potential Customers

Recommender systems are widely employed in many fields to recommend products, services and information to potential customers. As the most successful approach to recommender systems, collaborative filtering (CF) predicts user preferences in item selection based on the known user ratings of items. It can be divided into two main braches - the neighbourhood approach (NB) and latent factor models. Some of the most successful realizations of latent factor models are based on matrix factorization (MF). Accuracy is one of the most important measurement criteria for recommender systems. In this paper, to improve accuracy, we propose an improved MF model. In this model, we not only consider the latent factors describing the user and item, but also incorporate content information directly into MF.Experiments are performed on the Movielens dataset to compare the present approach with the other method. The experiment results indicate that the proposed approach can remarkably improve the recommendation quality.

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