A user-knowledge crowdsourcing task assignment model and heuristic algorithm for Expert Knowledge Recommendation Systems

Abstract

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.