A Collaborative Filtering Algorithm based on Citation Information
Tian Bai, Ye Wang, Lan Huang, Binzhao Ding, Jingbo Ning
Available Online July 2015.
- https://doi.org/10.2991/lemcs-15.2015.188How to use a DOI?
- Collaborative Filtering; Citation Information; Citation Network; Recommendation System
- Objective: With the rapid growing number of published scientific papers in the age of big data, users often find themselves difficult to select useful information from such massive academic information. This paper aims at the problems of collaborative filtering techniques in scientific citation data.Methods: This paper proposes an improved machine learning algorithm, that is designed to predict user ratings of academic theses by using Fisher Linear Regression combined with information of confidence scores, preference scores, the number of cooperative users in science citation data and the actual citation scores.Results: Multiple features considered in the algorithm have a positive impact on the recommendation results.Conclusion: This paper proposed a collaborative filtering recommendation algorithm combined with citation data analysis in order to improve the accuracy of predicted results. The experiments have shown that the proposed algorithm is proved to be effective and improve the accuracy of recommendation
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Tian Bai AU - Ye Wang AU - Lan Huang AU - Binzhao Ding AU - Jingbo Ning PY - 2015/07 DA - 2015/07 TI - A Collaborative Filtering Algorithm based on Citation Information BT - International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015) PB - Atlantis Press SP - 952 EP - 956 SN - 1951-6851 UR - https://doi.org/10.2991/lemcs-15.2015.188 DO - https://doi.org/10.2991/lemcs-15.2015.188 ID - Bai2015/07 ER -