基于深度学习的论文个性化推荐算法

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 content-based 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 outper-forms competing approches for the paper recommendation(+4% in terms of precision).