A Survey on Reviewer Assignment Problem 721 method known as LSI, Dumais and Nielsen [14] represented each manuscripts and reviewers'autobiography by a matrix containing nearly 100 item vectors of factors weight, the matching degree was computed as the dot product of the two matrixes Then, assignment was done by picking several reviewers from those with high match ing degree. A similar task was performed by Yarowsky and Florian [41], but review ers' biographies were replaced by their publications which were submitted by them- selves or downloaded from internet. Beginning with Dumais and Nielsens [14] paper, there are nine papers addressed the solution by using IR techniques. Table 1 reveals that content-based methodologies are acceptable, as text is the most important factor for manuscripts and reviewers' biographies, but not the only one for assignment Biswas and Hasan [5] compared the applicability of different content-based filterin and indicated that hybrid approaches might be a more comprehensive way Table 1. Review on the use of Ir solutions for rAp Methodology Yarowsky and Florian [41] Content-Based VSM/Naive Bayes Classifier Basu et al. [2] Popescul et al. [30] Hybrid Watanabe et al.[39 Collaborative Filtering Scale-free Network Hettich and Pazzani [21] Data Mining Data mining Rodriguez and Bollen [32] Collaborative Filtering Relative-rank Particle-Sw Algorithm Biswas and Hasan 5] Content-Based VSM( Comparison Study) Hvbrid Semantic web Studies by ir develop with the progresses of the technology itself. Content-based IR looks only at the contents of an artifact(e. g, the words on a paper), whereas col laborative filtering, which also consider the opinions of other like-minded people with respect to these artifacts, has been used to recommend NetNews articles [26], movies [1, 22], music[ll], and even jokes [18]. Scale-free network, which can continuously expand with the addition of new vertices, is a useful mechanism of collaborative fil tering. Watanabe et al. [39]had constructed a scale-free network whose vertices were keywords of reviewers'expertise and manuscripts' topic, and similarity between two keywords was the probability of connecting between the corresponding vertices. The matching degree is the weighted average of similarities between each pair of key words of manuscripts and reviewers. Instead of keywords, Rodriguez and Bollen [32] had approached a co-authorship network with vertices representing experts, edges representing a tie between two experts, and weights representing the strength of tie. with the application of collaborative filtering in other operations, a hybrid ap- roach was achieved before collaborative filtering by using co-authors and authors of reference to approach the collaborative method in the paper of Basu et al. [2]. Their framework provides a more flexible alternative to simple keyword-based search algo rithms and a less intrusive alternative to collaborative methods. Popescul et al. [30]A Survey on Reviewer Assignment Problem 721 method known as LSI, Dumais and Nielsen [14] represented each manuscripts and reviewers’ autobiography by a matrix containing nearly 100 item vectors of factors weight, the matching degree was computed as the dot product of the two matrixes. Then, assignment was done by picking several reviewers from those with high match￾ing degree. A similar task was performed by Yarowsky and Florian [41], but review￾ers’ biographies were replaced by their publications which were submitted by them￾selves or downloaded from internet. Beginning with Dumais and Nielsen’s [14] paper, there are nine papers addressed the solution by using IR techniques. Table 1 reveals that content-based methodologies are acceptable, as text is the most important factor for manuscripts and reviewers’ biographies, but not the only one for assignment. Biswas and Hasan [5] compared the applicability of different content-based filtering and indicated that hybrid approaches might be a more comprehensive way. Table 1. Review on the use of IR solutions for RAP Study Methodology Techniques Dumais and Nielsen [14] Content-Based Latent Semantic Indexing Yarowsky and Florian [41] Content-Based VSM/Naive Bayes Classifier Basu et al. [2] Hybrid N/A Popescul et al. [30] Hybrid N/A Watanabe et al. [39] Collaborative Filtering Scale-free Network Hettich and Pazzani [21] Data Mining Data Mining Rodriguez and Bollen [32] Collaborative Filtering Relative-rank Particle-swarm Algorithm Biswas and Hasan [5] Content-Based VSM (Comparison Study) Cameron et al. [6] Hybrid Semantic Web Studies by IR develop with the progresses of the technology itself. Content-based IR looks only at the contents of an artifact (e.g., the words on a paper), whereas col￾laborative filtering, which also consider the opinions of other like-minded people with respect to these artifacts, has been used to recommend NetNews articles [26], movies [1,22], music[11], and even jokes [18]. Scale-free network, which can continuously expand with the addition of new vertices, is a useful mechanism of collaborative fil￾tering. Watanabe et al. [39] had constructed a scale-free network whose vertices were keywords of reviewers’ expertise and manuscripts’ topic, and similarity between two keywords was the probability of connecting between the corresponding vertices. The matching degree is the weighted average of similarities between each pair of key￾words of manuscripts and reviewers. Instead of keywords, Rodriguez and Bollen [32] had approached a co-authorship network with vertices representing experts, edges representing a tie between two experts, and weights representing the strength of tie. With the application of collaborative filtering in other operations, a hybrid ap￾proach was achieved before collaborative filtering by using co-authors and authors of reference to approach the collaborative method in the paper of Basu et al. [2]. Their framework provides a more flexible alternative to simple keyword-based search algo￾rithms and a less intrusive alternative to collaborative methods. Popescul et al. [30]