%0 Conference Paper %1 liu2014robust %A Liu, Xiang %A Suel, Torsten %A Memon, Nasir %B Proceedings of the 8th ACM Conference on Recommender Systems %C New York, NY, USA %D 2014 %I ACM %K recommendation reviewer %P 25--32 %R 10.1145/2645710.2645749 %T A Robust Model for Paper Reviewer Assignment %U http://doi.acm.org/10.1145/2645710.2645749 %X Automatic expert assignment is a common problem encountered in both industry and academia. For example, for conference program chairs and journal editors, in order to collect "good" judgments for a paper, it is necessary for them to assign the paper to the most appropriate reviewers. Choosing appropriate reviewers of course includes a number of considerations such as expertise and authority, but also diversity and avoiding conflicts. In this paper, we explore the expert retrieval problem and implement an automatic paper-reviewer recommendation system that considers aspects of expertise, authority, and diversity. In particular, a graph is first constructed on the possible reviewers and the query paper, incorporating expertise and authority information. Then a Random Walk with Restart (RWR) 1 model is employed on the graph with a sparsity constraint, incorporating diversity information. Extensive experiments on two reviewer recommendation benchmark datasets show that the proposed method obtains performance gains over state-of-the-art reviewer recommendation systems in terms of expertise, authority, diversity, and, most importantly, relevance as judged by human experts. %@ 978-1-4503-2668-1

@inproceedings{liu2014robust, abstract = {Automatic expert assignment is a common problem encountered in both industry and academia. For example, for conference program chairs and journal editors, in order to collect "good" judgments for a paper, it is necessary for them to assign the paper to the most appropriate reviewers. Choosing appropriate reviewers of course includes a number of considerations such as expertise and authority, but also diversity and avoiding conflicts. In this paper, we explore the expert retrieval problem and implement an automatic paper-reviewer recommendation system that considers aspects of expertise, authority, and diversity. In particular, a graph is first constructed on the possible reviewers and the query paper, incorporating expertise and authority information. Then a Random Walk with Restart (RWR) [1] model is employed on the graph with a sparsity constraint, incorporating diversity information. Extensive experiments on two reviewer recommendation benchmark datasets show that the proposed method obtains performance gains over state-of-the-art reviewer recommendation systems in terms of expertise, authority, diversity, and, most importantly, relevance as judged by human experts.}, acmid = {2645749}, added-at = {2015-08-19T17:13:20.000+0200}, address = {New York, NY, USA}, author = {Liu, Xiang and Suel, Torsten and Memon, Nasir}, biburl = {https://www.bibsonomy.org/bibtex/22c0680d4e0e12221e4459c041e1d8a60/sdo}, booktitle = {Proceedings of the 8th ACM Conference on Recommender Systems}, description = {A robust model for paper reviewer assignment}, doi = {10.1145/2645710.2645749}, interhash = {3137071db0b8254a6d80520e954c9184}, intrahash = {2c0680d4e0e12221e4459c041e1d8a60}, isbn = {978-1-4503-2668-1}, keywords = {recommendation reviewer}, location = {Foster City, Silicon Valley, California, USA}, numpages = {8}, pages = {25--32}, publisher = {ACM}, series = {RecSys '14}, timestamp = {2015-08-19T17:13:20.000+0200}, title = {A Robust Model for Paper Reviewer Assignment}, url = {http://doi.acm.org/10.1145/2645710.2645749}, year = 2014 }