Trust-based Recommender System

System Features:

• Has a Simple GUI.
• Employs Trust between users to predict items ratings.
• Evaluate the results and compare them with the real data.

Steps to run the code:

1- Install latest python version.
2- From Windows command line/Mac terminal move to the project directory.
3- Type: python TrustRS.py
4- If any error appears reagarding missing packages then you may install them by typing:

pip install PySimpleGUI
pip install pandas
pip install numpy
pip install scipy
pip install sklearn

5- Run again.
6- The following window should appear:

7- Browse and select RS_Dataset folder, Set Review_sample, Trust_sample and Similarity sample files.
8- Click on Preprocess Data. Holdout is the percentage of the test set. The rest is used as a training set.
9- Select an approach (Refer to our paper to find out description of the approaches)
10- Compute trust values (usually takes 20-30 min.)
11- Predict Ratings.
12- Evaluate System, it will show the evaluation metrics results i.e MAE, MSE, RMSE. Also, it will show a comparison of the top 10 recommended items for both real and predicted ratings.

Developed as part of a Computer Science MSc course
Supervisor: Dr. Hafida Benhidour
Course: CSC590: Selected topics in computer applications
King Saud university
November 2020