Paper Info
Title
Car: Incorporating Filtered Citation Relations For Scientific Article Recommendation
Abstract
With the rapid proliferation of information technology, researchers have access to large archives of scientific articles. This makes it more challenging to find articles of interest for researchers. Consequently, a solution to this problem, scientific article recommendation, has become a hot research topic in recent years. In this paper, we propose a novel article recommendation method called Citation-based scientific Article Recommendation (CAR). CAR combines the information of researchers' historical preferences and citation relations between articles. We take into account the fact that, not all pairwise articles with citation relations are highly relevant although researchers generally find articles of interest by searching citations. Therefore, in our proposed method, weak citation relations are first filtered out through an association mining technique using data on researchers' historical preferences. Then, these filtered citation relations are incorporated into a graph-based article ranking method for enhancing recommendation quality. Through a relevant real-world dataset, we evaluate our proposed method. Our experimental results verify that the proposed method significantly outperforms other existing baseline methods in terms of precision, recall, and F1.
Year
DOI
Venue
2015
10.1109/SmartCity.2015.121
2015 IEEE INTERNATIONAL CONFERENCE ON SMART CITY/SOCIALCOM/SUSTAINCOM (SMARTCITY)
Keywords
Field
DocType
Article Recommendation, Collaborative Filtering, Random Walk, Citation Relations, Association Mining
Data science,Recommender system,Pairwise comparison,Graph,Ranking,Information retrieval,Computer science,Information technology,Citation,Association mining,Recall
Conference
Citations 
PageRank 
References 
1
0.35
16
Authors
6
Name
Order
Citations
PageRank
Haifeng Liu110.35
Zhuo Yang2233.17
Ivan Lee311516.92
Zhenzhen Xu48011.66
Shuo Yu5233.75
Feng Xia62013153.69