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题目: PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting

摘要:

在预测PM2.5浓度时,需要考虑复杂的信息源,因为PM2.5浓度在很长一段时间内会受到各种因素的影响。在本文中,我们识别了一组用于PM2.5预测的关键领域知识,并开发了一种新的基于图的模型PM2.5-GNN,该模型能够捕获长期相关性。在真实世界的数据集上,我们验证了所提出的模型的有效性,并检验了其捕获细粒度和长期影响PM2.5过程的能力。建议的PM2.5-GNN也已在网上部署,提供免费的预报服务。

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Popularity is often included in experimental evaluation to provide a reference performance for a recommendation task. To understand how popularity baseline is defined and evaluated, we sample 12 papers from top-tier conferences including KDD, WWW, SIGIR, and RecSys, and 6 open source toolkits. We note that the widely adopted MostPop baseline simply ranks items based on the number of interactions in the training data. We argue that the current evaluation of popularity (i) does not reflect the popular items at the time when a user interacts with the system, and (ii) may recommend items released after a user's last interaction with the system. On the widely used MovieLens dataset, we show that the performance of popularity could be significantly improved by 70% or more, if we consider the popular items at the time point when a user interacts with the system. We further show that, on MovieLens dataset, the users having lower tendencies on movies tend to follow the crowd and rate more popular movies. Movie lovers who rate a large number of movies, rate movies based on their own preferences and interests. Through this study, we call for a re-visit of the popularity baseline in recommender system to better reflect its effectiveness.

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