tauraiucb/volrec

Code for the paper "Session-based Recommendation with Temporal Dynamics for Large Volunteer Networks" published in the Journal of Intelligent Information Systems

Environment Settings

• Python 2.7.18
• Tensorflow 1.4.1
• Pandas 1.5.1
• Numpy 1.23.4

Usage

For experimentation, we provide raw data in the 'data' file which should be used as follows:

1. We first run the vol_network.py where the folder containing the input file should be specified. The output is a volunteer network.
2. The volunteer network and original file are inputs to the preprocess.py which has the following outputs:

• train.tsv: includes volunteer historical behaviors, which is organized by pandas.Dataframe in five fields (SessionId UserId ItemId Timestamps TimeId).

• valid.tsv: the same format as train.tsv, used for tuning hyperparameters.

• test.tsv: the same format as test.tsv, used for testing model.

• adj.tsv: includes links to volunteer networks, which is also organized by pandas.Dataframe in two fields (FromId, ToId).

• latest_session.tsv: serves as 'reference' to target volunteer. This file records all volunteers available session at each time slot. For example, at time slot t, it stores volunteer v's t-1 th session.

• user_id_map.tsv: maps original string of volunteer id to int.

• item_id_map.tsv: maps original string of organizer id to int.

• For shorthand and readability, we use User and Item to denote Volunteer and Organiser, respectively.

Running the code

• All output files from preprocess.py should be put in a data folder.
• To run the code, run sh volrec.sh

Data

• The Pioneers Volunteer Data has over 2.5 million events, 80 043 unique volunteers and 7 391 organizers.
• Full Pioneers data is open sourced at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YUOOBB

Reference

• Please cite our paper if you use this code or data in your own work

@article{muvunza2023session,
 title={Session-based recommendation with temporal dynamics for large volunteer networks},
 author={Muvunza, Taurai and Li, Yang},
 journal={Journal of Intelligent Information Systems},
 pages={1--22},
 year={2023},
 publisher={Springer}
}