计算机科学 ›› 2019, Vol. 46 ›› Issue (10): 27-31.doi: 10.11896/jsjkx.190300388
张琦, 柳玲, 文俊浩
ZHANG Qi, LIU Ling, WEN Jun-hao
摘要: 传统协同过滤算法存在数据稀疏与冷启动问题,社会化推荐算法虽然能在一定程度上缓解这些问题,但大多数的算法都只从单一的角度来衡量信任关系的影响。为了更准确地度量社交关系对推荐预测的影响,提出了一种基于领域信任及不信任的社会化奇异值分解(Field Trust and Distrust based Singular Value Decomposition,FTDSVD)推荐算法。该算法在SVD推荐算法的基础上加入了用户的信任关系与不信任关系,利用不信任关系对社交关系进行修正,并且充分考虑用户的信任领域相关性和全局影响力。在Epinions 数据集上将FTDSVD算法与相关算法进行了对比,结果证实了该算法在提高推荐质量和缓解冷启动问题上效果显著。
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