Abstract
In large online volunteer systems, inefficiency and low volunteer retention are existing challenges that compromise the success of online communities particularly given the uncertainty in volunteer participation behavior. A strategy that matches volunteers to a host of fields will alleviate these challenges, yet creating an all-in-one volunteer recommendation system is an unexplored but promising area. We propose VolRec, a session-based recommendation for large volunteer networks that employs temporal dynamics to capture uncertainty caused by the changing structure of volunteers’ participation behaviour. To optimize the recommendations, we construct a probabilistic volunteer network graph that denotes co-participation in an activity. We then model individual and inferred neighbours’ preferences as dynamic and context-aware sessions. VolRec can be adapted to recommend volunteers to organizers, tasks, groups and communities, creating a comprehensive and efficient recommendation system. Experiments using Pioneers data, a mobile based app launched in the wake of Covid-19 to mobilize volunteers and record their participation activities demonstrate the efficacy of this approach.
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The datasets generated during and/or analysed during the current study are available in the Harvard Dataverse repository, https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YUOOBB
Code Availability
The code and sample data used in this paper are available on our Github repository, https://github.com/TauraiUCB/VolRec
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Acknowledgements
We thank the Shenzhen Municipal Organizational Department for providing the volunteer data.
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This study is supported in part by the Tsinghua SIGS Scientific Research Start-up Fund (Grant Grant QD2021012C) and Natural Science Foundation of China (Grant 62001266)
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Muvunza, T., Li, Y. Session-based recommendation with temporal dynamics for large volunteer networks. J Intell Inf Syst 61, 901–922 (2023). https://doi.org/10.1007/s10844-023-00801-4
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DOI: https://doi.org/10.1007/s10844-023-00801-4