[null, {"document_url": "https://link.springer.com/chapter/10.1007%2F978-3-319-39958-4_27", "unformatted_src": "https://media.springernature.com/w306/springer-static/cover/book/978-3-319-39958-4.jpg", "src": "https://media.springernature.com/w306/springer-static/cover/book/978-3-319-39958-4.jpg"}, null]


Web-Age Information Management International Conference on Web-Age Information Management

WAIM 2016: Web-Age Information Management pp 342-355 | Cite as

AdaWIRL: A Novel Bayesian Ranking Approach for Personal Big-Hit Paper Prediction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9659)


Predicting the most impactful (big-hit) paper among a researcher’s publications so it can be well disseminated in advance not only has a large impact on individual academic success, but also provides useful guidance to the research community. In this work, we tackle the problem of given the corpus of a researcher’s publications in previous few years, how to effectively predict which paper will become the big-hit in the future. We explore a series of features that can drive a paper to become the big-hit, and design a novel Bayesian ranking algorithm AdaWIRL (Adaptive Weighted Impact Ranking Learning) that leverages a weighted training schema and an adaptive timely false correction strategy to predict big-hit papers. Experimental results on the large ArnetMiner dataset with over 1.7 million authors and 2 million papers demonstrate the effectiveness of AdaWIRL. Specifically, it correctly predicts over 78.3 % of all researchers’ big-hit papers and outperforms the compared regression and ranking algorithms, with an average of \(5.8\,\%\) and \(2.9\,\%\) improvement respectively. Further analysis shows that temporal features are the best indicator for personal big-hit papers, while authorship and social features are less relevant. We also demonstrate that there is a high correlation between the impact of a researcher’s future works and their similarity to the predicted big-hit paper.


Latent Dirichlet Allocation Citation Count Average Citation Ranking Algorithm Academic Feature



This work was partially supported by the National Natural Science Foundation of China (No. 11305043, No. 61433014), and the Zhejiang Provincial Natural Science Foundation of China (No. LY14A050001), the EU FP7 Grant 611272 (project GROWTHCOM) and Zhejiang Provincial Qianjiang Talents Project (Grant No. QJC1302001). Chuxu Zhang thanks to the assistantship of Computer Science Department of Rutgers University and Internship Experience of IBM Thomas J. Watson Research Center.



© Springer International Publishing Switzerland 2016



Zhang C., Yu L., Lu J., Zhou T., Zhang ZK. (2016) AdaWIRL: A Novel Bayesian Ranking Approach for Personal Big-Hit Paper Prediction. In: Cui B., Zhang N., Xu J., Lian X., Liu D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science, vol 9659. Springer, Cham



