MCLRE - Multi-Contextual Learning to Rank Events

The MCLRE is an event recommendation model that takes into account multiple contextual informations (i.e. social, geographical, content and time) to recommend events in event-based social networks. It was presented in the paper Context-Aware Event Recommendation in Event-based Social Networks published on RecSys 2015. The paper presentation at RecSys’15 is also available in YouTube.

This repository contains the MCLRE experimental framework and you can use it to reproduce our experiments and also to evolve the model.

Requirements

Linux: all experiments were executed in Linux-based machines, Ubuntu distributions, more specifically
Database: deploy a local Postgres database and use it as the initial data source to the data partitioning phase.
Python: run sudo pip install -r src/requirements.txt
R: Every library is going to be installed on demand by the framework

Running

Follow the steps in the file: src/run_experiment.sh

Dataset

For dataset access you should ask Leandro Balby Marinho (lbalby_at_gmail_dot_com), he will send it promptly.

Thank you for the interest and have a good job!

Citing

@inproceedings{Macedo:2015:CER:2792838.2800187,
 author = {Macedo, Augusto Q. and Marinho, Leandro B. and Santos, Rodrygo L.T.},
 title = {Context-Aware Event Recommendation in Event-based Social Networks},
 booktitle = {Proceedings of the 9th ACM Conference on Recommender Systems},
 series = {RecSys '15},
 year = {2015},
 isbn = {978-1-4503-3692-5},
 location = {Vienna, Austria},
 pages = {123--130},
 numpages = {8},
 url = {http://doi.acm.org/10.1145/2792838.2800187},
 doi = {10.1145/2792838.2800187},
 acmid = {2800187},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {algorithms, event-based social networks, experimentation, recommender systems},
}

Ackowledgments

This work was partially supported by the National Institute of Science and Technology for Software Engineering (INES), funded by CNPq and FACEPE, grants 573964/2008-4 and APQ-1037-1.03/08; and Hewlett-Packard Brasil Ltda., through the FRH-Analytics 2013 project, and used incentives from the Brazilian Informatics Law (n. 8.2.48/1991). We also want to thank Lucas Drumond for generously sharing the implementation of his MRBPR method.

MCLRE is free software (open source software), it can be used and distributed under the terms of the GNU General Public License (GPL).