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A user-knowledge crowdsourcing task assignment model and heuristic algorithm for Expert Knowledge Recommendation Systems
Engineering Applications of Artificial Intelligence ( IF 8 ) Pub Date : 2020-09-21 , DOI: 10.1016/j.engappai.2020.103959
Li Gao , Yi Gan , Binghai Zhou , Mengyu Dong

Expert Knowledge Recommendation System (EKRS) is a scientific research assistant system that actively provides experts with the latest domain knowledge according to their professional knowledge background. This paper applies the crowdsourcing task assignment method, taking experts as users and recommending corresponding professional knowledge as tasks. To solve the problems of inaccurate user-knowledge matching and low assignments in EKRS, a user-knowledge task assignment model is established. To maximize the number of global task assignments, the model first applies an improved greedy assignment algorithm to convert the user-knowledge task maximum assignment problem into the maximum weight problem in bipartite graphs. Based on the matching value between a task and a user, a task is assigned to the user with a high matching value. Then, the assigned tasks are sorted with the tree decomposition technique to obtain the optimal task scheduling scheme. The heuristic depth-first search algorithm (DFS+HA) is used to update the boundaries of the heuristic function quickly, and the assignment scheme of the optimal solution can be obtained efficiently through the upper and lower bounds of the search process. Finally, the algorithm was experimentally verified with artificial data sets and the real data extracted from EKRS. The experimental results indicated that the algorithm proposed in this paper can improve the amount of user-knowledge task assignment in EKRS, find the optimal assignment scheme to maximize the number of global task assignments, and improve the search efficiency.



中文翻译:

专家知识推荐系统的用户知识众包任务分配模型和启发式算法

专家知识推荐系统(EKRS)是一种科学研究辅助系统,可根据其专业知识背景主动为专家提供最新的领域知识。本文采用众包任务分配方法,以专家为用户,并推荐相应的专业知识作为任务。为了解决EKRS中用户知识匹配不准确,任务分配少的问题,建立了用户知识任务分配模型。为了最大化全局任务分配的数量,该模型首先应用改进的贪婪分配算法,将用户知识任务最大分配问题转换为二部图中的最大权重问题。基于任务和用户之间的匹配值,以高匹配值将任务分配给用户。然后,用树分解技术对分配的任务进行排序,以获得最优的任务调度方案。启发式深度优先搜索算法(DFS + HA)用于快速更新启发式函数的边界,并且可以通过搜索过程的上下限有效地获得最优解的分配方案。最后,通过人工数据集和从EKRS提取的真实数据对算法进行了实验验证。实验结果表明,本文提出的算法可以提高EKRS中的用户知识任务分配数量,找到最优的分配方案,以最大化全局任务分配数量,提高搜索效率。启发式深度优先搜索算法(DFS + HA)用于快速更新启发式函数的边界,并且可以通过搜索过程的上下限有效地获得最优解的分配方案。最后,通过人工数据集和从EKRS提取的真实数据对算法进行了实验验证。实验结果表明,本文提出的算法可以提高EKRS中的用户知识任务分配数量,找到最优的分配方案,以最大化全局任务分配数量,提高搜索效率。启发式深度优先搜索算法(DFS + HA)用于快速更新启发式函数的边界,并且可以通过搜索过程的上下限有效地获得最优解的分配方案。最后,通过人工数据集和从EKRS提取的真实数据对算法进行了实验验证。实验结果表明,本文提出的算法可以提高EKRS中的用户知识任务分配数量,找到最优的分配方案,以最大化全局任务分配数量,提高搜索效率。通过搜索过程的上下边界可以有效地获得最优解的分配方案。最后,通过人工数据集和从EKRS提取的真实数据对算法进行了实验验证。实验结果表明,本文提出的算法可以提高EKRS中的用户知识任务分配数量,找到最优的分配方案,以最大化全局任务分配数量,提高搜索效率。通过搜索过程的上下边界可以有效地获得最优解的分配方案。最后,通过人工数据集和从EKRS提取的真实数据对算法进行了实验验证。实验结果表明,本文提出的算法可以提高EKRS中的用户知识任务分配数量,找到最优的分配方案,以最大化全局任务分配数量,提高搜索效率。

更新日期:2020-09-21
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