Update README.md

README.md

@@ -9,3 +9,5 @@ The implementation of system can be represented through three steps:
 The application was developed in Python 3.6 and is based on four libraries: LightFM - is used to build the recommender system; Keras - is used for feature extraction from the movies posters; Scikit learn - is used to create clusters; Skopt - is used to optimize the model parameters. The application was run on Intel i7 Quad Core, 2.60 Ghz with 16GB RAM and with a swap memory extension up to 70GB.
 
 From the point of view of results we got an improvement of the precision@k and accuracy metrics. If is used just the movies posters, precision is improvement with 0.42\% and accuracy with 0.32\%. If posters is used together with genres, precision is improvement with 0.82\% and accuracy with 1.09\%.
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+Paper can be found [here](https://github.com/adiIspas/King-Recommender-Paper).