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A previous project based on scikit-learn in COMP 5212, HKUST. The project includes collaborative filtering based recommendation system (Matrix Factorization).

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Collaborative_Filtering_Based_Recommendation_System

please refer to the report.pdf

If you have some questions, I would like to discuss~ ^_^ (tliang at ust dot hk)

The project consists of the following tasks:

  1. To implement PMF with maximum a posteriori (MAP) estimation according to the estimator interface of scikit-learn.
  2. To conduct empirical study to compare the performance of PMF by varying the number of latent factors K and the regularization hyperparameters λu and λv .
  3. To conduct empirical study to compare the performance of PMF on dense and sparse data.

command examples:

python prj3.py --docv --sparse

python prj3.py

python prj3.py --largeinput

python prj3.py --largeinput --docv

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A previous project based on scikit-learn in COMP 5212, HKUST. The project includes collaborative filtering based recommendation system (Matrix Factorization).

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