摘要
随着知识爆炸时代的到来,电子文献数据库的负荷将急剧扩大,用户在库中搜寻所需资源也将越发困难。因此,开发电子文献资源推荐系统从而辅助电子数据库的管理受到研究者的广泛重视。协同过滤作为时下数据库的常用推荐技术,由于仅仅考虑了用户对于文章的历史评分的相似度,忽略了用户在语义层面和社交关系的距离等重要因素因而推荐效果有限。为了在推荐系统中融入这些影响因素,本文在基于用户的协同过滤的方法基础上引入了基于主题模型的文本相似度和两种社会化的用户相似度(用户标签相似度与用户群组相似度),运用非监督的融合策略对这些相似度进行了整合。本文提出的融合文本特征与社会化指标的方法在真实数据集上展示了多源信息对于推荐准确度的增强和提升效应,对于电子文献资源的管理和传播具有较强的启示意义。
With the arrival of the era of information expansion, the load on electronic literature databases will dramatically increase, and it will become increasingly difficult for users to search for their required pieces of literature. In response to this issue, the development of recommendation systems to assist the management of electronic literature databases has received extensive attention from researchers. Currently, one commonly used recommendation technique for literature databases is collaborative filtering. However, the traditional collaborative filtering algorithms, which only consider the similarity of users’search-history, ignore several important factors, such as the users’semantic similarity and social relationships. In this paper, we integrated a text content similarity based on topic model as well as two kinds of user similarities based on social relationships (user tag similarity and personal group similarity) into the user collaborative filtering recommender system by utilizing an unsupervised integration strategy. The experiment on the real data set shows that by adding the multiple source features, there is an enhancement and promotion effect on the recommendation accuracy, which provides strong implications for related electronic literature resource recommendation research in the future.
作者
杨辰
刘婷婷
刘雷
牛奔
孙见山
Yang Chen;Liu Tingting;Liu Lei;Niu Ben;Sun Jianshan(College of Management,Shenzhen University,Shenzhen 518060;School of Management,Hefei University of Technology,Hefei 230009)
出处
《情报学报》
CSSCI
CSCD
北大核心
2019年第6期632-640,共9页
Journal of the China Society for Scientific and Technical Information
基金
国家自然科学基金青年科学基金项目“基于科研社交网络挖掘的专家组合推荐问题的研究”(71701134)
国家自然科学基金面上基金项目“基于复杂适应菌群优化的新型港口布局、泊位与岸吊分配联合决策”(71571120)
教育部人文社会科学研究基金“基于在线科研社交平台的合作者推荐研究”(16YJC630153)
广东省自然科学基金博士启动项目(2017A030310427)
关键词
文献资源
资源推荐
协同过滤
主题模型
社交网络
literature resources
resource recommendation
collaborative filtering
topic models
social networks
