Version 2.0 of IBGR Group Recommendation Model

This release marks the second version (2.0) of the IBGR Group Recommendation Model, a Python implementation inspired by the research of Reza Barzegar Nozari and Hamidreza Koohi on group recommendation. In this update, we've introduced an innovative group creation method, as proposed in the paper, enhancing the capabilities of our recommendation system.

Reference:
Reza Barzegar Nozari and Hamidreza Koohi. (2020). "A novel group recommender system based on members’ influence and leader impact." Knowledge-Based Systems, 205, 106296. https://doi.org/10.1016/j.knosys.2020.106296

Usage Instructions:
Please read the README.

Please provide feedback and contributions to improve this implementation. Feel free to explore, modify, and adapt the code to your needs. Your feedback is valuable in enhancing the effectiveness and usability of this group recommender system.

Release Date: Sep. 27, 2023

Version 1.0: Initial Release - IBGR: Group Recommendation Model

This is the first version (1.0) of the IBGR Group Recommendation Model, a Python implementation of the group recommendation model proposed by Reza Barzegar Nozari and Hamidreza Koohi. The model, inspired by their research paper "A novel group recommender system based on members’ influence and leader impact," aims to provide personalized item recommendations for groups while considering the influence of group members and leader impact.

Key Features:

• Calculates influenced ratings for group members based on similarity and trust
• Evaluates recommendations using various metrics (accuracy, precision, recall, specificity, F1-score, etc.)
• Implements the IBGR algorithm for group recommendation
• Provides a clear structure for easy understanding and customization

Reference:
Reza Barzegar Nozari and Hamidreza Koohi. (2020). "A novel group recommender system based on members’ influence and leader impact." Knowledge-Based Systems, 205, 106296. https://doi.org/10.1016/j.knosys.2020.106296

Usage Instructions:

1. Clone or download the repository.
2. Create Virtualenv.
3. Install requirements by command: pip install -r requirements.txt.
4. Modify the data source to match your dataset.
5. Run 'IBGR.py' to calculate influenced ratings, evaluate recommendations, and save results.

Please provide feedback and contributions to improve this implementation. Feel free to explore, modify, and adapt the code to your needs. Your feedback is valuable in enhancing the effectiveness and usability of this group recommender system.

Release Date: Aug. 23, 2023