Session-based recommendation with temporal dynamics

Session-based organizer recommendation for volunteers

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 framework 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.


Taurai Muvunza and Yang Li, Session-based recommendation with temporal dynamics for large volunteer networks, Journal of Intelligent Information Systems, 2023 pdf ppt
  title={Session-based recommendation with temporal dynamics for large volunteer networks},
  author={Muvunza, Taurai and Li, Yang},
  journal={Journal of Intelligent Information Systems},

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