Repository files navigation Movie Topic Modeling based on NLP and Recommendation System Design
Used tokenizing, stemming and stop-words removing to preprocess user review text, and extracted features via TF-IDF
Trained unsupervised learning models including K-Means Clustering and Latent Dirichlet Allocation to implement Latent Topic Clustering
Visualized clustering results by dimensionality reduction using Principal Component Analysis.
Analysis of the movie rating dataset and information extraction
Collaborative Filtering on Spark implementation( ALS model )
Personal Movie Recommendation
MSE Curves analysis and parameter tuning
Local Collaborative Filtering
ALS Collaborative Filtering Algorithm
MSE Curves analysis and parameter tuning
ROC Curves Analysis
Recommendation_Spark_Scala
encoding or raw rating data
itembased collaborative filtering algorithm
tune parameter for lower RMSE
Recommendation based on AutoEncoder-decoder Model
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