一种改进List-wise的科技论文推荐方法研究

Study of improved List-wise method for scientific paper recommendation

近些年,科研社交网站中的科技论文数量呈现出爆炸式增长的趋势,用户很难发现符合自己要求的科技论文,而科技论文推荐正是解决这个问题的有效方法之一。但是现有科技论文推荐方法大多专注于评分预测的准确性,忽视了推荐科技论文之间的排序问题,并且现有的科技论文推荐方法没有充分利用科研社交网站中的社会化信息。为此,提出了一种改进List-wise的科技论文推荐方法,系统地分析了科研社交网站中的好友关系,科技论文的标题、摘要和标签等社会化信息,并将其融入到List-wise方法中。为了验证提出方法的有效性,抓取了科研社交网站Cite ULike上的数据进行验证,实验结果表明,与其他传统的推荐方法相比,该方法取得了较好的实验结果,具有良好的可扩展性。

In recent years, the number of papers in scientific social network had grown at an explosive rate. It was difficult for users to find papers related to their requirement. And the paper recommendation is one of the key methods to solve this problem. However, most of the existing methods only focus on rating prediction, ignoring the ranking problem between papers. In addition, a lot of social information in scientific social network were not fully considered in the traditional recommendation method. Therefore, this paper proposed an improved List-wise method for scientific paper recommendation. It systematically analyzed The friendship information between users, the titles, abstracts and tags of scientific papers and incorporated theses information into the improved List-wise method. In order to verify the validity of the proposed method, this paper crawled data from a scientific social network, i.e., CiteULike, to conduct experiments. Experimental results show that the proposed method gets the best recommendation results and performs well in scalability compared to the other traditional recommendation methods.