Paper Review of Recommender Systems
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Updated
May 30, 2018
Paper Review of Recommender Systems
Restaurant Recommendation Systems based on the Yelp dataset (2019) using Ensemble method based on Images and text from reviews.
An implementation for https://ojs.aaai.org/index.php/AAAI/article/view/4448
A library of recommender systems with collaborative, content-based filtering, and hybrid models.
Code store for custom implementation of some machine learning algorithms from scratch.
Build a Recommender Engine using Amazon SageMaker Factorization Machines
nimfm: A library for factorization machines in Nim
A high-performance toolkit for LR/FM training on large-scale sparse data.
Code of my master thesis "Implicit Feedback Based Context-Aware Recommender For Music Light System"
Factorization machine implemented in TensorFlow 2
Notes on papers related to factorization machines
Build and evaluate classification model using PySpark 3.0.1 library.
Julia wrapper for pyCFM(Convex Factorization Machines)
Technical writings on Deep Learning
The primary objective of this study is to explore the feasibility of using machine learning algorithms to classify health insurance plans based on their coverage for routine dental services. To achieve this, I used six different classification algorithms: LR, DT, RF, GBT, SVM, FM(Tech: PySpark, SQL, Databricks, Zeppelin books, Hadoop, Spark-Submit)
Recommendation System using Factorization Machines - AWS SageMaker NoteBook Instance
Quantifying NBA player interactions
Running field-aware factorization machines on the Criteo data
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