论文标题
TADER：项目管理平台的新任务依赖性建议
TaDeR: A New Task Dependency Recommendation for Project Management Platform
论文作者
论文摘要
全球许多初创公司和公司一直在使用项目管理软件和工具来监视，跟踪和管理其项目。对于软件项目，从头到尾的任务数量是一个很大的数量，有时需要花费大量时间和精力将当前任务链接到以前的任务以进行进一步参考。本文提出了一种有效的任务依赖性建议算法，以建议取决于用户刚创建的给定任务的任务。我们提出了一个有效的功能工程步骤，并为此目标构建了深层神经网络。我们对两个不同的大型项目（来自moodle.org的mdlsite和apache.org的Flume）进行了广泛的实验，以使用两种嵌入方法（手套和fastText）找到28个功能组合和最佳性能模型中的最佳功能。我们使用准确性@k，mrr@k和回忆@K（其中k = 1、2、3和5）和基线模型考虑了三种类型的模型（GRU，CNN，LSTM），并使用传统方法：TF-IDF：具有各种匹配分数的TF-IDF，例如余弦相似性，Euclidean speconity，euclidean dameants，Manhattan dame，曼哈顿（Manhattan）距离，和Chebyshev距离。经过许多实验，手套嵌入和CNN模型在我们的数据集中达到了最佳结果，因此我们选择了该模型作为建议的方法。此外，在后处理步骤中添加时间过滤器可以显着改善建议系统的性能。实验结果表明，我们所提出的方法可以在@1中达到0.2335，而在数据集水槽的1@1中，MRR@1和0.2011。使用MDLSITE数据集，我们获得了0.1258的准确性@1，MRR@1和0.1141在Recement@1中获得了0.1141。在前5名中，我们的模型在5、5.2563 MRR@5中达到0.3040，而0.2651在Flume中召回@5。在MDLSite数据集中，我们的模型获得了0.5270的精度@5、0.2689 MRR@5和0.2651 Relest@5。
Many startups and companies worldwide have been using project management software and tools to monitor, track and manage their projects. For software projects, the number of tasks from the beginning to the end is quite a large number that sometimes takes a lot of time and effort to search and link the current task to a group of previous ones for further references. This paper proposes an efficient task dependency recommendation algorithm to suggest tasks dependent on a given task that the user has just created. We present an efficient feature engineering step and construct a deep neural network to this aim. We performed extensive experiments on two different large projects (MDLSITE from moodle.org and FLUME from apache.org) to find the best features in 28 combinations of features and the best performance model using two embedding methods (GloVe and FastText). We consider three types of models (GRU, CNN, LSTM) using Accuracy@K, MRR@K, and Recall@K (where K = 1, 2, 3, and 5) and baseline models using traditional methods: TF-IDF with various matching score calculating such as cosine similarity, Euclidean distance, Manhattan distance, and Chebyshev distance. After many experiments, the GloVe Embedding and CNN model reached the best result in our dataset, so we chose this model as our proposed method. In addition, adding the time filter in the post-processing step can significantly improve the recommendation system's performance. The experimental results show that our proposed method can reach 0.2335 in Accuracy@1 and MRR@1 and 0.2011 in Recall@1 of dataset FLUME. With the MDLSITE dataset, we obtained 0.1258 in Accuracy@1 and MRR@1 and 0.1141 in Recall@1. In the top 5, our model reached 0.3040 in Accuracy@5, 0.2563 MRR@5, and 0.2651 Recall@5 in FLUME. In the MDLSITE dataset, our model got 0.5270 Accuracy@5, 0.2689 MRR@5, and 0.2651 Recall@5.
