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Reza Barzegar Nozari's Projects

ibgr icon ibgr

IBGR (Influence-Based Group Recommendation) is a novel group recommendation that published in Knowledge-Based System journal at Elsevier. It takes into account the influence of members and leaders in groups to determine items rating proper to all members for groups. There is a sample MATLAB code of IBGR for a fixed group with 4 member and 7 items.

ibgr-group-recommendation-model icon ibgr-group-recommendation-model

This repository contains a Python script that implements a novel group recommender system based on the research paper titled "A novel group recommender system based on members’ influence and leader impact" by Reza Barzegar Nozari and Hamidreza Koohi.

implicit-trust-network-based-recommendation-methodology icon implicit-trust-network-based-recommendation-methodology

The codes have been provided to support the article "Novel implicit-trust-network-based recommendation methodology", which has been published in Expert Systems With Applications. This algorithmic framework is abbreviated to ITNRM. It first generates implicit trust networks to find users' trustees or neighbors. And a novel recommendation methodology is implemented to find suitable items closed to the users' preferences. Finally, datasets, i.e., MovieLens, Ciao, and FilmTrust are used for experiments.

itnrm-implicit-trust-network-based-recommendation-methodology icon itnrm-implicit-trust-network-based-recommendation-methodology

This repository contains Python code that implements a trust-based collaborative filtering approach for enhancing rating predictions in recommendation systems. The methodology is based on the research paper titled "Novel Implicit-Trust-Network-based Recommendation Methodology" by Reza Barzegar Nozari and Hamidreza Koohi.

knn-classifier-for-predicting-customers-purchase-behavior icon knn-classifier-for-predicting-customers-purchase-behavior

This repository contains two implementations of a K-Nearest Neighbors (KNN) classifier for predicting online shopping behavior. The classifiers are implemented in Python and use different approaches for finding the nearest neighbors: Naive Implementation, KDTree Implementation

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