A new approach in designing and developing a collaborative-interactive movie recommender system based on user ratings
For Download the dataset just click on the link: https://files.grouplens.org/datasets/movielens/ml-25m.zip
In this research, we first reviewed past works related to movie recommender systems. Then we developed our proposed method, in which we used the TF-IDF criterion to transform data and the similarity criterion to obtain the common tastes of users with similar tastes.
Also, to evaluate the proposed model, we have used two measures:
- Root Mean Square Error (RMSE)
- Mean absolute error (MAE)
The results were slightly improved in comparison with the other 3 models