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《Acta Electronica Sinica》 2017-04
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Workflow Activity Recommendation by Collaborative Filtering

CHEN Guang-zhi;HE Wen;LI Lei;School of Data and Computer Science,Sun Yat-sen University;  
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.
【Fund】: 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)
【CateGory Index】: TP391.3
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