https://github.com/seyedhosseinzadeh/sucp

Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation
https://github.com/seyedhosseinzadeh/sucp

matrix-factorization point-of-interest python27 recommendation-system social-influence social-network

Last synced: about 1 month ago
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Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

• Host: GitHub
• URL: https://github.com/seyedhosseinzadeh/sucp
• Owner: Seyedhosseinzadeh
• Created: 2020-09-01T06:23:16.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2022-11-21T10:46:43.000Z (almost 2 years ago)
• Last Synced: 2024-10-12T09:20:11.227Z (about 1 month ago)
• Topics: matrix-factorization, point-of-interest, python27, recommendation-system, social-influence, social-network
• Language: Python
• Homepage:
• Size: 11.6 MB
• Stars: 13
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# SUCP

## Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation (Information Processing & Management Journal_2022)

Direct Friends (i.e., users who follow each other in an LBSN) and Distant Friends (i.e., users with commonly visited check-ins) usually have close opinions, even some friendships are made because of these behavioral similarities. Our analysis reveals the social behavior pattern of users for geographic activity centers. This paper proposes a new approach that examines user's preferences based on three contextual factors: geographical, social, and temporal information. we compare the performance of our SUCP with its variant, called SUCP-NoSocial. 

you can read the [paper](https://www.sciencedirect.com/science/article/pii/S0306457321003290) for more details

## Environment Settings

- Python version:  '2.7'

- You have to install the required libraries

## To run the code

You need just run the `recommendation.py` then enter data-name and beta value, like this: ' gowalla 0.7 '

- To change the dataset, you have to write its name in the `recommendation.py`.

- Note that use 0.7 for the Gowalla beta and 0.8 for the Yelp betta, according to the paper.

## Cite

Please cite our paper if you use our datasets or implementations:

This repository contains the implementation of Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation presented in the IPM 2022 paper. 

# Contact

If you have any questions, do not hesitate to contact us at '[email protected]' or   '[email protected]', we will be happy to assist.