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A Survey of Latent Factor Models for Recommender Systems and Personalization

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posted on 07.01.2021, 10:09 by Shalin ShahShalin Shah

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.

History

Email Address of Submitting Author

sshah100@jhu.edu

ORCID of Submitting Author

0000-0002-3770-1391

Submitting Author's Institution

Johns Hopkins University

Submitting Author's Country

United States of America