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Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L. and Su, Z. (2008) ArnetMiner: Extraction and Mining of Academic Social Networks. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, SIGKDD’2008, Las Vegas, 24-27 August 2008, 990-998.
http://dx.doi.org/10.1145/1401890.1402008

has been cited by the following article:

• TITLE: Incorporating User’s Preferences into Scholarly Publications Recommendation

  AUTHORS: Tobore Igbe, Bolanle Ojokoh

  KEYWORDS: Personalization, Digital Library, Information Retrieval, Recommender System, Citation Analysis, User Preferences

  JOURNAL NAME: Intelligent Information Management, Vol.8 No.2, March 29, 2016

  ABSTRACT: Over the years, there has been increasing growth in academic digital libraries. It has therefore become overwhelming for researchers to determine important research materials. In most existing research works that consider scholarly paper recommendation, the researcher’s preference is left out. In this paper, therefore, Frequent Pattern (FP) Growth Algorithm is employed on potential papers generated from the researcher’s preferences to create a list of ranked papers based on citation features. The purpose is to provide a recommender system that is user oriented. A walk through algorithm is implemented to generate all possible frequent patterns from the FP-tree after which an output of ordered recommended papers combining subjective and objective factors of the researchers is produced. Experimental results with a scholarly paper recommendation dataset show that the proposed method is very promising, as it outperforms recommendation baselines as measured with nDCG and MRR.