Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Graph-based ranking algorithms for e-mail expertise analysis
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Probabilistic models for expert finding
ECIR'07 Proceedings of the 29th European conference on IR research
Full-text citation analysis: enhancing bibliometric and scientific publication ranking
Proceedings of the 21st ACM international conference on Information and knowledge management
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The idea behind AuthorRank is that a content created by more popular authors should rank higher than the content created by less popular authors. This paper brings this idea into scientific publications analysis to test whether the optimized topical AuthorRank can replace or enhance topical PageRank for publication ranking. First, the PageRank with Priors (PRP) algorithm was employed to rank topic-based publications and authors. Second, the first author's reputation was used for generating an AuthorRank score. Additionally, linear combination method of topical AuthorRank and PageRank were compared with several baselines. Finally, as shown in our evaluation results, the performance of topical AuthorRank combined with topic-based PageRank is better than other baselines for publication ranking.