A Survey of Latent Factor Models for Recommender Systems and Personalization

10.36227/techrxiv.13513974 ◽

2021 ◽

Author(s):

Shalin Shah

Keyword(s):

Recommender Systems ◽

Matrix Factorization ◽

Matrix Completion ◽

Factor Models ◽

Singular Value ◽

Bayesian Personalized Ranking ◽

Latent Factor Models ◽

Value Decomposition ◽

Personalized Ranking ◽

Non Negative Matrix Factorization

<p>Recommender systems aim to personalize the experience of a user and are critical for businesses like retail portals, e-commerce websites, book sellers, streaming movie websites and so on. The earliest personalized algorithms use matrix factorization or matrix completion using algorithms like the singular value decomposition (SVD). There are other more advanced algorithms, like factorization machines, Bayesian personalized ranking (BPR), and a more recent Hebbian graph embeddings (HGE) algorithm. In this work, we implement BPR and HGE and compare our results with SVD, Non-negative matrix factorization (NMF) using the MovieLens dataset.</p>

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Introduction to Matrix Factorization for Recommender Systems

10.31219/osf.io/pnd5w ◽

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.

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Content-Enhanced Matrix Factorization for Recommender Systems

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.475-476.1084 ◽

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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