PLRS: Personalized Literature Hybrid Recommendation System with Paper Influence

被引：2
|
作者：
Liu, Fanghan [1 ]
Cai, Wenzheng [1 ]
Ma, Kun [1 ]
机构：
[1] Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan 250022, Peoples R China
来源：
ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2020, PT III | 2020年 / 12454卷
基金：
中国国家自然科学基金;
关键词：
Cold start; Literature recommendation; Hybrid recommendation algorithm; Keyword extraction;
D O I：
10.1007/978-3-030-60248-2
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
The traditional management platform of academic literature depends on the active retrieval of users, and the accuracy of literature retrieval results cannot be guaranteed. In our demonstration, we introduce the personalized literature hybrid recommendation system with paper influence (PLRS for short). The innovation of PLRS is that it can calculate the influence of literature, address the issue of cold start, and actively provide users with high-quality literature in line with users' interests combined with cascade hybrid recommendation algorithm.
引用
收藏
页码：703 / 705
页数：3
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