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
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.
A high-performance toolkit for LR/FM training on large-scale sparse data.
nimfm: A library for factorization machines in Nim
Julia wrapper for pyCFM(Convex Factorization Machines)
Technical writings on Deep Learning
Quantifying NBA player interactions
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
基于混合推荐算法的文学作品推荐系统-算法后端
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