ScholarNodes: Applying Content-based Filtering to Recommend Interdisciplinary Communities within Scholarly Social Networks

被引:0
作者
Noor, Asaduzzaman [1 ]
Clark, Jason A. [1 ]
Sheppard, John W. [1 ]
机构
[1] Montana State Univ, Bozeman, MT 59717 USA
来源
PROCEEDINGS OF THE 47TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, SIGIR 2024 | 2024年
关键词
Recommender systems; Collaboration recommendations; Cross domain recommendation; Network visualization; Social network analysis; Interface Design;
D O I
10.1145/3626772.3657668
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Detecting communities within dynamic academic social networks and connecting these community detection findings to search and retrieval interfaces presents a multifaceted challenge. We explore an information retrieval method that integrates both partitionbased and similarity-based network analysis to identify and recommend communities within content-based datasets. Our prototype "ScholarNodes" web interface bridges the gap between community detection algorithms (Louvain, K-means, Spectral clustering) and the BM25 (Best Matching 25) ranking algorithm within a cohesive user interface. From free-text keyword queries, ScholarNodes recommends collaborations, identifies local and external researcher networks, and visualizes an interdisciplinarity graph for individual researchers using the OpenAlex dataset, a global collection of academic papers and authors. Beyond the specific information retrieval use case, we discuss the broader applicability of the methods to generic social network analysis, community detection, and recommender systems. Additionally, we delve into the technical aspects of generating topical terms, community alignment techniques, and interface design considerations for integrating community detection algorithms into a search experience.
引用
收藏
页码:2791 / 2795
页数:5
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