南京大学学报(自然科学版) ›› 2018, Vol. 54 ›› Issue (6): 11611170.doi: 10.13232/j.cnki.jnju.2018.06.012
魏 桐,童向荣*
Wei Tong,Tong Xiangrong*
摘要: 在社交媒体中,信任传递在用户交互关系的建立上发挥着至关重要的作用. 实际应用中,通常将信任传递过程应用于推荐系统来预测起始用户对特定目标用户的信任程度,从而更好地作出下一步决策. 选择的信任路径是否较优与预测的准确性息息相关. 针对路径长度和信任值在整条路径上的值分布,提出一种新的加权启发式搜索信任预测模型. 该模型将改进的经典A*算法应用于信任网络进行路径寻找,其中,改进的A*算法在寻路过程中使用了二次启发并将启发函数设置为筛选条件进行路径筛选. 该模型最终得到的信任路径具有相对较好的鲁棒性,相对提高了预测的准确性,而且在信任累加计算中融入了信任的衰减. 最后,通过对比实验验证该模型的有效性,并分析了参数的变化对结果预测的影响.
中图分类号:
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