Recommendation method for academic journal submission based on doc2vec and XGBoost

• Published in: Scientometrics Vol. 127; no. 5; pp. 2381 - 2394
• Main Authors: ZhengWei, Huang, JinTao, Min, YanNi, Yang, Jin, Huang, Ye, Tian
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.05.2022; Springer Nature B.V
• Subjects: Algorithms; Bibliographies; Classification; Computer Science; Information Storage and Retrieval; Library Science; Recommender systems; Representations; Scientific papers; Semantics; Document bibliography; XGBoost; Journal recommendation; Doc2vec; Machine learning
• ISSN: 0138-9130; 1588-2861
• DOI: 10.1007/s11192-022-04354-1

With the continuous deepening of academic research in various disciplines and the continuous increase in the number of scientific researchers, exploring the mechanism of matching scientific research results and academic journal subjects is a key topic that can assist researchers in selecting suitable journals for submission. The classification and recommendation of academic journals based on a traditional text representation model cannot take advantage of the semantic relationship between words and cannot take into account the diversity of topics received by different journals, which affects the classification and recommendation effect. To solve these problems, this paper uses doc2vec to perform distributed representation of the bibliographic text so that the semantics between the text features are fully preserved. Then, the XGBoost algorithm is used to consider the impact of the different characteristics of the title, abstract, and keywords of the bibliography on the published journal. The academic journal submission recommendation model proposed in this paper can solve the problem that traditional methods cannot make full use of the contextual semantic information and improve the efficiency of scientific research personnel's academic achievement publications. Experiments on Common SCI English journals in the computer field show that when recommending three candidate journals, the accuracy rate reached 84.24%.