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Session-based recommendation with temporal dynamics for large volunteer networks

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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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Data Availability

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

References

  • An, M., Wu, F., Wu, C., et al. (2019). Neural news recommendation with long-and short-term user representations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 336–345. https://doi.org/10.18653/v1/P19-1033

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 993–1022.

  • Choi, M., Kim, J., Lee, J., et al. (2022). S-Walk: accurate and scalable session-based recommendation with random walks. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 150–160. https://doi.org/10.1145/3488560.3498464

  • Donkers, T., Loepp, B., & Ziegler, J. (2017). Sequential user-based recurrent neural network recommendations. In: Proceedings of the eleventh ACM conference on recommender systems, pp. 152–160. https://doi.org/10.1145/3109859.3109877

  • Eagle, N., Pentland, A., & Lazer, D. (2008). Mobile phone data for inferring social network structure. In: Social Computing, Behavioral Modeling, and Prediction, pp. 79–88. https://doi.org/10.1007/978-0-387-77672-9_10

  • Fan, W., Ma, Y., Li, Q., et al. (2019). Graph neural networks for social recommendation. In The World Wide Web Conference, pp. 417–426. https://doi.org/10.1145/3308558.3313488

  • Gao, C., Zheng, Y., Li, N., et al. (2023). A survey of graph neural networks for recommender systems: Challenges, methods, and directions. ACM Transactions on Recommender Systems, 1(1), 1–51. https://doi.org/10.1145/3568022

    Article  Google Scholar 

  • Gao, C., Wang, X., He, X., et al. (2022). Graph neural networks for recommender system. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 1623–1625. https://doi.org/10.1145/3488560.3501396

  • Guo, L., Yin, H., Wang, Q., et al. (2019). Streaming session-based recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1569–1577. https://doi.org/10.1145/3292500.3330839

  • Hamilton, W., Ying, Z., & Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30

  • He, X., Liao, L., Zhang, H., et al. (2017). Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182. https://doi.org/10.1145/3038912.3052569

  • He, Y., Mao, Y., Xie, X., et al. (2022). An improved recommendation based on graph convolutional network. Journal of Intelligent Information Systems, 59(3), 801–823. https://doi.org/10.1007/s10844-022-00727-3

    Article  Google Scholar 

  • Hidasi, B., Karatzoglou, A., Baltrunas, L., et al. (2015). Session-based recommendations with recurrent neural networks. arXiv:1511.06939

  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv:1412.6980

  • Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv:1609.02907

  • Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. https://doi.org/10.1109/MC.2009.263

    Article  Google Scholar 

  • Lei, J., Li, Y., Yang, S., et al. (2022). Two-stage sequential recommendation for side information fusion and long-term and short-term preferences modeling. Journal of Intelligent Information Systems, 59, 657–677. https://doi.org/10.1007/s10844-022-00723-7

    Article  Google Scholar 

  • Li, J., Ren, P., Chen, Z., et al. (2017). Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1419–1428. https://doi.org/10.1145/3132847.3132926

  • Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 7(1), 76–80. https://doi.org/10.1109/MIC.2003.1167344

    Article  Google Scholar 

  • Liu, J., Dolan, P., & Pedersen, E. R. (2010). Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, pp. 31–40. https://doi.org/10.1145/1719970.1719976

  • Liu, Q., Zeng, Y., Mokhosi, R., et al. (2018). STAMP: short-term attention/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1831–1839. https://doi.org/10.1145/3219819.3219950

  • Locke, M., Ellis, A., & Smith, J. D. (2003). Hold on to what you’ve got: the volunteer retention literature. Voluntary Action, 5(3), 81–99.

    Google Scholar 

  • Marston, C., Renedo, A., & Miles, S. (2020). Community participation is crucial in a pandemic. The Lancet, 395(10238), 1676–1678. https://doi.org/10.1016/S0140-6736(20)31054-0

    Article  Google Scholar 

  • McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444. https://doi.org/10.1146/annurev.soc.27.1.415

    Article  Google Scholar 

  • Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010). Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811–820. https://doi.org/10.1145/1772690.1772773

  • Rendle, S., Freudenthaler, C., Gantner, Z., et al. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618

  • Sarwar, B., Karypis, G., Konstan, J., et al. (2001). Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. https://doi.org/10.1145/371920.372071

  • Shani, G., Heckerman, D., Brafman, R. I., et al. (2005). An MDP-based recommender system. Journal of Machine Learning Research, 6(9)

  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

    Article  MathSciNet  Google Scholar 

  • Song, C., Wang, B., Jiang, Q., et al. (2021). Social recommendation with implicit social influence. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1788–1792. https://doi.org/10.1145/3404835.3463043

  • Song, W., Xiao, Z., Wang, Y., et al. (2019). Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 555–563. https://doi.org/10.1145/3289600.3290989

  • Truong, C., Oudre, L., & Vayatis, N. (2020). Selective review of offline change point detection methods. Signal Processing, 167, 107299. https://doi.org/10.1016/j.sigpro.2019.107299

    Article  Google Scholar 

  • Veličković, P., Cucurull, G., Casanova, A., et al. (2017). Graph attention networks. arXiv:1710.10903

  • Wang, S., Zhang, Q., Hu, L., et al. (2022). Session-based Recommendations: Challenges, Approaches, Applications and Opportunities. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3425–3428. https://doi.org/10.1145/3477495.3532685

  • Wang, Z., Liao, J., Cao, Q., et al. (2014). Friendbook: a semantic-based friend recommendation system for social networks. IEEE Transactions on Mobile Computing, 14(3), 538–551. https://doi.org/10.1109/TMC.2014.2322373

    Article  Google Scholar 

  • Wu, S., Tang, Y., Zhu, Y., et al. (2019). Session-based recommendation with graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 346–353. https://doi.org/10.1609/aaai.v33i01.3301346

    Article  Google Scholar 

  • Xu, K., Li, C., Tian, Y., et al. (2018). Representation learning on graphs with jumping knowledge networks. In: International Conference on Machine Learning, pp. 5453–5462.

  • Zhang, A., Zhang, K., Li, W., et al. (2022). Optimising self-organised volunteer efforts in response to the COVID-19 pandemic. Humanities and Social Sciences Communications, 9(1). https://doi.org/10.1057/s41599-022-01127-2

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Acknowledgements

We thank the Shenzhen Municipal Organizational Department for providing the volunteer data.

Funding

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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  1. Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, University Town, Shenzhen, 518055, Guangdong, China

    Taurai Muvunza & Yang Li

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  1. Taurai Muvunza
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  2. Yang Li
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Correspondence to Yang Li.

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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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