A Recommender System for Crowdsourcing Food Rescue Platforms

A Recommender System for Crowdsourcing Food Rescue Platforms
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众包食品救援平台的推荐系统

DOI:
10.1145/3442381.3449787
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发表时间:
2021
期刊:
WWW '21: Proceedings of the Web Conference 2021
影响因子:
--
通讯作者:
Fang, Fei
Fang, Fei
中科院分区:
--
文献类型:
--
作者:
Shi, Zheyuan Ryan;Lizarondo, Leah;Fang, Fei

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粮食浪费和不安全的挑战在富裕国家和发展中国家都出现了,影响到数百万人的生计。目前的流行病只会使问题更加严重。作为打击粮食浪费和不安全的主要力量,粮食救援组织将粮食捐赠与为低资源社区服务的非营利组织相匹配。由于他们依靠外部志愿者来领取和运送食物,一些FR使用基于网络的移动的应用程序来接触合适的志愿者。在本文中,我们提出了第一个基于机器学习的模型,以提高志愿者在食物浪费和安全领域的参与度。我们(1)开发了一个推荐系统,为每个给定的救援发送推送通知给最有可能的志愿者,(2)利用基于数学规划的方法来多样化我们的建议,(3)提出了一个在线算法来动态选择志愿者通知未来救援的知识。我们的推荐系统提高了命中率从44%达到以前的方法到73%。计划在不久的将来对我们的方法进行试点研究。
The challenges of food waste and insecurity arise in wealthy and developing nations alike, impacting millions of livelihoods. The ongoing pandemic only exacerbates the problem. A major force to combat food waste and insecurity, food rescue (FR) organizations match food donations to the non-profits that serve low-resource communities. Since they rely on external volunteers to pick up and deliver the food, some FRs use web-based mobile applications to reach the right set of volunteers. In this paper, we propose the first machine learning based model to improve volunteer engagement in the food waste and security domain. We (1) develop a recommender system to send push notifications to the most likely volunteers for each given rescue, (2) leverage a mathematical programming based approach to diversify our recommendations, and (3) propose an online algorithm to dynamically select the volunteers to notify without the knowledge of future rescues. Our recommendation system improves the hit ratio from 44% achieved by the previous method to 73%. A pilot study of our method is scheduled to take place in the near future.
提高基于志愿者的粮食救援行动的效率
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