计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 158-164.doi: 10.11896/jsjkx.210500013
郭亮, 杨兴耀, 于炯, 韩晨, 黄仲浩
GUO Liang, YANG Xing-yao, YU Jiong, HAN Chen, HUANG Zhong-hao
摘要： 将用户评论和用户评分相结合来提升推荐系统的性能是推荐系统当前主流的研究方向,但是当用户评论数据稀疏时,现有的大多数推荐系统的性能会出现一定幅度的下降。针对这一问题,文中提出了一种结合注意力机制和门控网络形成的混合推荐系统(Attention Mechanism and Gating Network-based Recommendation System,AMGNRS)。该模型利用志趣相投的用户所产生的辅助评论来缓解用户评论的稀疏性问题,首先将多种混合注意力机制相结合来提高提取用户评论及评分的特征的效率,然后通过门控网络自适应地融合提取的特征并选出与用户偏好最相关的特征,最后利用神经因子分解机的高阶线性相互作用来推导评分预测。将所提模型与当前表现优异的模型在3个真实数据集上进行了对比实验,结果表明,所提模型显著地缓解了数据的稀疏性问题,验证了其有效性。
中图分类号:
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