Journal Recommendation System Using Content-Based Filtering

被引：9
|
作者：
Jain, Sonal [1 ]
Khangarot, Harshita [1 ]
Singh, Shivank [1 ]
机构：
[1] JK Lakshmipat Univ, Dept Comp Sci Engn, Inst Engn & Technol, Jaipur, Rajasthan, India
来源：
RECENT DEVELOPMENTS IN MACHINE LEARNING AND DATA ANALYTICS | 2019年 / 740卷
关键词：
Journal recommendation system; Preprocessing; Single-value decomposition; Euclidean distance;
D O I：
10.1007/978-981-13-1280-9_9
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Recommendation systems provide an approach to facilitate the user's desire. It is helpful in recommending the things from various domains. Researchers express their ideas and experience in an academic article for the research community. However, they have ample of options when they aspire to publish. At times, they end up with incorrect submission resulting in waste of time and effort of editor as well as himself. Journal selection has been a very tedious task for the novice authors. In this paper, Journal Recommendation System (JRS) is proposed, which will solve the problem of publication for many authors. Content-based filtering method is used for this purpose. The dataset used is prepared by the authors and distance algorithm is used for recommendation.
引用
收藏
页码：99 / 108
页数：10
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