摘要
该文基于学术搜索和数据挖掘平台Aminer向用户进行个性化推荐,提出了结合协同过滤推荐和基于内容推荐的混合模型,实验表明该算法可以有效解决新物品的推荐问题,即冷启动问题。其中在基于内容推荐的模型中,融合深度学习的方法,引进了词向量模型,将用户和论文映射到用词向量空间,并使用WMD(Word Mover Distance)计算相似度。实验表明,与其他基线模型相比该文提出的推荐模型在准确率上显著提高了4%。
In this paper,we propose a personalized paper recommender system based on Aminer,an academic search and data mining platform.We propose a hybrid recommender system combining collaborative filtering and contentbased recommendation.Further,we boost the performance of our model by incorporating word embedding and word mover distance(WMD)in content-based recommendation.The experiments show that we can signifieantly outperforms competing approches for the paper recommendation(+4%in terms of precision).
作者
王妍
唐杰
WANG Yan;TANG Jie(Department of Computer Science and Technology of Tsinghua University, Beijing 100084, China)
出处
《中文信息学报》
CSCD
北大核心
2018年第4期114-119,共6页
Journal of Chinese Information Processing
关键词
个性化推荐
协同过滤
词向量
personalized recommendation
collaborative filtering
word embedding