采用协同过滤技术进行工作流活动推荐  

Workflow Activity Recommendation by Collaborative Filtering

作　　者：陈广智[1] 何文[1] 李磊[1] 

机构地区：[1]中山大学数据科学与计算机学院,广东广州510006

出　　处：《电子学报》2017年第4期890-897,共8页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61300095);广东省自然科学基金(No.S2012040011123);广东省教育厅高校优秀青年创新人才培育(No.2012LYM-0065)

摘　　要：为解决企事业单位的流程变动问题,利用正常实例和异常实例信息向当前不完整实例推荐下一可能执行的活动.由于每个工作流实例是一个活动名称序列,它们不能直接参与数值运算,需首先将序列中每个活动出现的顺序以数值的形式表示出来,最终将实例库转换成矩阵形式,该矩阵类似于推荐系统中的User-Item矩阵,以便于实例间相似度计算.最后,从实例库中筛选出与当前不完整实例相似性高的完整实例,利用这些实例的信息构造出活动列表,作为推荐结果.实验结果及对比分析表明:我们的活动推荐算法是可行的和有效的.To address the problem of changes of business processes for an enterprise or organization,we utilize the normal and exceptional instances to recommend the next possible activity for the current incomplete workflow instance. Since every workflow instance is a sequence of activity names, it cannot be calculated numerically. we firstly extract the order of each activity in the sequence as a number value, and then get a matrix which is similar to User-Item matrix in traditional recommendation systems. This matrix can facilitate the calculation of similarity between two workflow instances. Finally,we choose these complete instances which are most similar to the current incomplete instance, construct the activity list as the recommendation result by these instances. Experimental results show that the proposed algorithm is effective and efficient.

关 键 词：协同过滤 活动推荐 推荐系统 工作流活动 工作流实例 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]