计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 114-120.doi: 10.11896/jsjkx.190900038
康雁, 卜荣景, 李浩, 杨兵, 张亚钏, 陈铁
KANG Yan, BU Rong-jing, LI Hao, YANG Bing, ZHANG Ya-chuan, CHEN Tie
摘要: 推荐系统是解决信息过载问题的核心。现有的推荐框架研究面临着显式反馈数据稀疏和数据预处理难等问题,特别是对新用户和新项目进行推荐的性能有待进一步提高。随着深度学习的推进,基于深度学习的推荐成为了当前的研究热点,大量的实验证明了深度学习运用于推荐系统的有效性。文中在NCF的基础上提出了EANCF(Neural Collaborative Filtering based on Enhanced-Attention Mechanism),从隐式反馈数据的角度研究了推荐框架,利用最大池化、局部推理以及组合多种不同数据融合方式来考虑数据特征提取;同时,引入注意力机制来为网络合理地分配权重值,减少信息的损失,提升推荐的性能。最后,基于两个大型真实数据集Movielens-1m和Pinterest-20对EANCF、NCF和部分经典算法做了对比实验,并且详细地给出了EANCF框架的训练过程。实验结果表明,EANCF框架确实具有较好的推荐性能,相比于NCF框架在HR@10和NDCG@10上均有显著提升,HR@10最高提升了3.53%,NDCG@10最高提升了2.47%。
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