722 F Wang B Chen and Z miao proposed a unified probabilistic framework for combining content-based and collabo- rative IR by extending Hofmann's [23] aspect model to incorporate the information source among scholar, manuscript and manuscript content. Semantic Web technology was also brought to collect data, represent researches'expertise and co-authorship and find relevant reviewers in a peer-review setting [6]. As the increase of internet resource, data mining can be used to identify relative reviewers within an existing expert pool by mining online information through search engines, potential external reviewers can also be found in the online academic community In respond to the difficulty for a scientific community to agree up k eywords and maintain such a keyword database over time, Hettich and Pazzani [21] described the data mining method deployed at the U.S. NSF for assisting program directors in identifying reviewers for proposals. To the best of our knowledge, thei paper is the only one work found in literature which combined IR and optimization to achieve rap 4 Reviewer Assignment Optimization Optimization on reviewer assignment which mainly focus on the theory, modeling algorithm of assigning manuscripts to reviewers, can be viewed as an enhanced version of the Generalized Assignment Problem(GAP). However, most of these stud- ies are not based on the matching degree evaluated by IR, but simply in sense of simi- larity by the selected keywords. In making it easier to read, the notations used in the cited sources have, where necessary, been changed to try to keep consistent as those of traditional gap The RAP optimization we study is the following. We are given a set P=(l,,P) of manuscripts and a set R=(lr) of reviewers; and a parameter cy denoting the matching degree"of manuscript i for reviewer j, where iE ter a, is the certain number of reviewers that manuscript i should be assigned to: Parameter b, is the certain number of manuscripts that reviewer j should be as- signed to no more than; a given threshold T can be set as boundary of c to identify reviewers'qualification o The simplest version of RAP just distinguishes whether each reviewer and each anuscript can be matched or not, and represents the matching degree as a px matrix C=Ci(i=lp,j=lr), where cy is a binary parameter whose value equals l if there is overlap between reviewers expertise and manuscript's topic and no conflicts of interest, otherwise 0. As the binary ci is too general to present the grade of matching, discrete matching degree becomes a more popular method. A typical but exhausted way is requiring reviewers to rate their preference according the abstracts, with I for the lowest preference to 10 for the highest, sometimes p is used for the highest, where p is the total number of manuscripts. Once there is con flict of interest, cy is set to 0 [24]. Instead of rating for all the submissions, reviewers are usually required to score their expertise level on different disciplines(keywords)722 F. Wang, B. Chen, and Z. Miao proposed a unified probabilistic framework for combining content-based and collabo￾rative IR by extending Hofmann’s [23] aspect model to incorporate the information source among scholar, manuscript and manuscript content. Semantic Web technology was also brought to collect data, represent researches’ expertise and co-authorship, and find relevant reviewers in a peer-review setting [6]. As the increase of internet resource, data mining can be used to identify relative reviewers within an existing expert pool by mining online information through search engines, potential external reviewers can also be found in the online academic community. In respond to the difficulty for a scientific community to agree upon taxonomy of keywords and maintain such a keyword database over time, Hettich and Pazzani [21] described the data mining method deployed at the U.S. NSF for assisting program directors in identifying reviewers for proposals. To the best of our knowledge, their paper is the only one work found in literature which combined IR and optimization to achieve RAP. 4 Reviewer Assignment Optimization Optimization on reviewer assignment which mainly focus on the theory, modeling and algorithm of assigning manuscripts to reviewers, can be viewed as an enhanced version of the Generalized Assignment Problem (GAP). However, most of these stud￾ies are not based on the matching degree evaluated by IR, but simply in sense of simi￾larity by the selected keywords. In making it easier to read, the notations used in the cited sources have, where necessary, been changed to try to keep consistent as those of traditional GAP. The RAP optimization we study is the following. We are given a set P = {1,..., p} of manuscripts and a set R = {1,...,r} of reviewers; and a parameter ij c denoting the “matching degree” of manuscript i for reviewer j , where i ∈ P and j ∈ R . Parame￾ter i a is the certain number of reviewers that manuscript i should be assigned to; Parameter bj is the certain number of manuscripts that reviewer j should be as￾signed to no more than; a given threshold T can be set as boundary of ij c to identify reviewers’ qualification. The simplest version of RAP just distinguishes whether each reviewer and each manuscript can be matched or not, and represents the matching degree as a p × r matrix ij C = c ( i = 1,..., p , j = 1,...,r ), where ij c is a binary parameter whose value equals 1 if there is overlap between reviewer’s expertise and manuscript’s topic and no conflicts of interest, otherwise 0. As the binary ij c is too general to present the grade of matching, discrete matching degree becomes a more popular method. A typical but exhausted way is requiring reviewers to rate their preference according to the abstracts, with 1 for the lowest preference to 10 for the highest, sometimes p is used for the highest, where p is the total number of manuscripts. Once there is con￾flict of interest, ij c is set to 0 [24]. Instead of rating for all the submissions, reviewers are usually required to score their expertise level on different disciplines (keywords)