用共同作者等四类相似度推荐论文，提升科研选题效率。
Utilizing Collaborative Filtering in a Personalized Research-Paper Recommendation System
- 融合作者、关键词、参考文献等多维度相似度计算
- 基于目标用户找到top-n相似用户，推荐相关论文
- 适合需要高效追踪研究前沿的学者使用
推荐系统能帮助用户在短时间内精准获取所需内容，其基础是基于相似用户或物品的偏好。在数字化时代，互联网提供了海量开放资源，但从中精准筛选却十分困难。研究论文推荐系统旨在为具有共同研究兴趣的用户提供服务，采用协同过滤方法。本文通过杰卡德相似度（Jaccard Similarity）计算共同作者、关键词、参考文献及共同引文的相似性，综合得出最终相似度，并据此找出目标用户的 top-n 相似用户，进而生成论文推荐。实验结果表明，该系统具有显著的推荐准确率，效果令人印象深刻。
原文摘要 · Abstract (English)
Recommendation system is such a platform that helps people to easily find out the things they need within a few seconds. It is implemented based on the preferences of similar users or items. In this digital era, the internet has provided us with huge opportunities to use a lot of open resources for our own needs. But there are too many resources on the internet from which finding the precise one is a difficult job. Recommendation system has made this easier for people. Research-paper recommendation system is a system that is developed for people with common research interests using a collaborative filtering recommender system. In this paper, coauthor, keyword, reference, and common citation similarities are calculated using Jaccard Similarity to find the final similarity and to find the top-n similar users. Based on the test of top-n similar users of the target user research paper recommendations have been made. Finally, the accuracy of our recommendation system has been calculated. An impressive result has been found using our proposed system.
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