知识找回场景下推荐系统模拟实现及评价研究

Purpose/significance] Information overload has been always considered as the major barrier confronted by knowledge workers in the process of gathering, processing and producing information. One of its consequences is that it is hard to recall documents that ever used, while the recommendation system could reduce such difficulty. Comparing the recommendation efficiencies through representative recommendation mechanisms may assisst knowledge workers in accomplishing the task of knowledge re-finding.[Method/process] Based on associated recommendation system theoies, this paper presents a simulation on 4 different recommendation procedures in an unified experimental scene(knowledge re-finding), the precedures includes CBR, CFR, INR and CAS. 4 evaluation criteria (precision, context-relevance, action-prediction, diversity) has been used to evaluate and compare the efficiency of corresponding recommendation systems.[Result/conclusion] The results show that each recommendation procedure has its own advantages in knowledge re-finding from different perspectives, and CAS has advantages in both context-relevance and action-prediction.