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data/ml-latest-small
DESCRIPTION.yaml
Makefile
README.rst
SAR_0.0.1.tar.gz
configure.R
data.R
demo.R
movie-recommender.R
print.R
sar_model.RData
score.R
train.R

README.rst

Movie Recommendation

The MovieLens data sets are popular data sets for building recommendation models in research, education and development. The dataset (ml-latest-small) used in this demonstration contains 100,004 5-star ratings across 9125 movies created by 671 users between 9 January 1995 and 16 October 2016. The dataset records the userId, movieId, rating, timestamp, title, and genres. The goal is to build a recommendation model to recommend new movies to users.

This pre-built model has used the R language to build a recommendation model to represent the knowledge discovered using a Smart Adaptive Recommendations (SAR) algorithm. The knowledge representation is easy to understand.

The demo command applies the pre-built model to a demo data set with records from 10 users and shows the recommendation results for 2 users.

The print command will display a textual summary of the model and its build parameters.

The score command applies the pre-built model to a supplied data set and shows the recommendation results for 2 users. Example score command:

$ ml score movie-recommender user10.csv