计算机科学 ›› 2019, Vol. 46 ›› Issue (10): 32-38.doi: 10.11896/jsjkx.180901801
温雯1, 林泽钿1, 蔡瑞初1, 郝志峰1,2, 王丽娟1
WEN Wen1, LIN Ze-tian1, CAI Rui-chu1, HAO Zhi-feng1,2, WANG Li-juan1
摘要: 传统的刻画用户偏好的方法主要着眼于用户的长期兴趣,然而在现实应用中,用户兴趣随着时间迁移而不断变化,如何挖掘用户在时序上的动态偏好仍然面临挑战。为此,文中提出了一种基于嵌入学习的动态行为预测方法。首先,利用改进的词嵌入模型从用户的点击行为序列中学习获得每一个点击项的低维向量表示;然后,基于所学习的向量表示,结合用户近期点击行为推断用户的动态偏好,进而预测其下一步的点击行为。在两个真实数据集上将提出的方法与近年出现的其他基准方法进行比较,结果表明,所提方法在预测准确率上具有明显的优势。
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