With the huge amount of digital information available on the internet, it becomes a challenge for users to access items efficiently. Recommender systems are information filtering systems that deal with the problem of digital data overload to pull out items or information according to the user's preferences, interests, and behavior, inferred from previous activities.
- Latent factorization-based collaborative filtering
- Using deep learning for latent factor collaborative filtering
- Using the restricted Boltzmann machine for building recommendation systems
- Learn the technical knowhows
[Data] (https://grouplens.org/datasets/movielens/)
- File name (100K Movie Lens)
Latent Factor Collaborative Filtering using Deep Learning.ipynb
SVD++.ipynb
python preprocess_ratings.py --path '/home/santanu/ML_DS_Catalog-/Collaborating Filtering/ml-100k/' --infile 'u.data'
python rbm.py main_process --mode train --train_file '/home/santanu/ML_DS_Catalog-/Collaborating Filtering/ml-100k/train_data.npy' --outdir '/home/santanu/ML_DS_Catalog-/Collaborating Filtering/' --num_hidden 5 --epochs 1000
python rbm.py main_process --mode test --train_file '/home/santanu/ML_DS_Catalog-/Collaborating Filtering/pred_all_recs.csv' --test_file '/home/santanu/ML_DS_Catalog-/Collaborating Filtering/ml-100k/test_data.npy' --outdir '/home/santanu/ML_DS_Catalog-/Collaborating Filtering/' --user_info_file '/home/santanu/ML_DS_Catalog-
/Collaborating Filtering/ml-100k/u.user' --movie_info_file '/home/santanu/ML_DS_Catalog-/Collaborating Filtering/ml-100k/u.item'
These are sample commands and need to be changed accordingly based on data repositories,output directory,etc