基于学术社交网络用户关系的文献搜索推荐研究

Research on Paper Search Recommendation Based on User Relationships in Academic Social Networks

[目的/意义]挖掘与利用学术社交网络数据,为用户推荐个性化的文献搜索结果,为增强与优化知识服务平台功能提供新的思路。[方法/过程]本文以学术社交网络为基础,构建其中学术用户间的关注关系网络,通过将度数声望与最短距离结合提出推荐指标。文献搜索推荐以特定用户视角展开,在利用该用户自身生成内容进行第一次搜索内容扩展后,计算出候选学者列表并用于第二次搜索扩展和文献相似度计算。[结果/结论]本文选择ResearchGate为学术社交网络数据来源,爬取“Artificial Intelligence”主题下的用户关系以及用户生成内容数据,推荐结果匹配用户搜索意图且所涉及学术领域更为详细,与用户自述的学术兴趣吻合度较高。

[Purpose/Significance]The purpose of this paper is to mine academic social network data,recommend personalized paper search results for researchers,and provide new ideas for enhancing and optimizing the functions of the knowledge service platform.[Method/Process]Based on the academic social network,this paper constructed a following relationship network among academic users,and proposes recommendation indicators by combining the degree of prestige with the shortest distance.The research was carried out from the perspective of a certain academic user.After the user s user-generated content was used for the first search content expansion,a list of candidate researchers was calculated and used for the second search content expansion to more accurately match the researcher s search intent and interest.[Result/Conclusion]This paper chosen ResearchGate as the source of the academic social network data,crawled user relationships and user-generated content data related to the topic of“Artificial Intelligence”.The academic field involved in the final recommendation result is more detailed,which is more consistent with the academic interest expressed by the certain user.