计算机科学 ›› 2022, Vol. 49 ›› Issue (10): 126-131.doi: 10.11896/jsjkx.220700064
任胜兰1, 郭慧娟2, 黄文豪3, 汤志宏4, 亓慧1
REN Sheng-lan1, GUO Hui-juan2, HUANG Wen-hao3, TANG Zhi-hong4, Qi Hui1
摘要: 为了捕捉在线购物时用户与商品之间的动态交互关系,提高推荐系统(RS)的准确度,提出了结合用户倾向性和商品吸引力的用户评价预测方法。首先,将评论分为用户评论文本和商品评论文本,分别输入两个交互卷积神经网络(CNN),并结合注意力机制,动态捕捉文本中的语义信息和上下文信息,得到用户和商品的自适应特征;然后,利用交互注意力网络,分析商品特征和用户特征的动态交互关系,计算出用户对特定商品的倾向性和商品对特定用户的吸引力;最后,通过预测模块提供用户对商品的准确评价预测。在数据集上进行实验,结果表明,所提方法取得了最优性能,比其他方法的MAE和RMSE性能分别至少提升了15.1%和13.6%。此外,基于Top-K的统计指标进一步验证了所提方法的商品推荐精准度。
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