Recommendation system is information filtering system that seek to predict the rating or preference that a certain query shows relevancy to particular item or document. In this thesis, we propose a recommendation system which assists journal and conference editors to find suitable reviewer for the proposal. Here, we have focused on domain classification issue of proposal recommendation system using academic tag comparison. We used different source of domain knowledge base called “Call for papers (CFP)”. Based on the keywords provided in CFPs we build domain to classify proposal and reviewers in particular domain. We used Boolean and Vector Space Model concept to find out relevant domain as well as relevant reviewers and ranked them. In our experiment we used real world dataset from National Science Council (NSC) Taiwan, which comprises of 724 proposals (Year 99) and 1253 reviewers. The experimental result shows that our system performs marginally better than previous Expert Finding System.

Recommendation system is information filtering system that seek to predict the rating or preference that a certain query shows relevancy to particular item or document. In this thesis, we propose a recommendation system which assists journal and conference editors to find suitable reviewer for the proposal. Here, we have focused on domain classification issue of proposal recommendation system using academic tag comparison. We used different source of domain knowledge base called “Call for papers (CFP)”. Based on the keywords provided in CFPs we build domain to classify proposal and reviewers in particular domain. We used Boolean and Vector Space Model concept to find out relevant domain as well as relevant reviewers and ranked them. In our experiment we used real world dataset from National Science Council (NSC) Taiwan, which comprises of 724 proposals (Year 99) and 1253 reviewers. The experimental result shows that our system performs marginally better than previous Expert Finding System.

1) C. Basu , W. W. Cohen, H. Hirsh, and C. Nevill-Manning, “Technical Paper Recommendation: A Study in Combining Multiple Information Sources”, Journal Of Artificial Intelligence Research, Volume 14, pages 231-252, 2001
2) A. Bellogin, J. Wang, and P. Castells, “Text retrieval methods for item ranking in collaborative filtering”. In ECIR 2011. LNCS, vol. 6611, pp. 301–306. Springer, Heidelberg (2011)
3) D. Bollegala, Y. Matsuo, and M. Ishizuka, “Measuring semantic similarity between words using web search engines”, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada.
4) P. Castells, M. Fernandez, and D. Vallet, "An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval," Knowledge and Data Engineering, IEEE Transactions on , vol.19, no.2, pp.261-272, Feb. 2007
5) C.-H. Chen, C.-Y. Lu, H.-M. Lee, and J.-M. Ho, "Novelty Paper Recommendation Using Citation Authority Diffusion", The 2011 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2011)
6) Y.-N. Chen, Yu Huang, Sheng-Yi Kong, and Lin-Shan Lee, "Automatic key term extraction from spoken course lectures using branching entropy and prosodic/semantic features," Spoken Language Technology Workshop (SLT), 2010 IEEE , vol., no., pp.265-270, 12-15 Dec. 2010
7) J. Choi, C. Choi, D. Choi, J. koh, and P. Kim, “Semantic Relation Extraction for Automatically Building Domain Ontology using a Link Grammar” . Proceedings of the 2011 ACM Symposium on Research in Applied Computation, RACS’11, November 2-5, 2011 Miami, Florida, USA.
8) C.-C. Chou, K.-H. Yang, and H.-M. Lee, "AEFS: Authoritative Expert Finding System Based on a Language Model and Social Network Analysis," TAAI 2007
9) C. Gennaro, G. Amato, P. Bolettieri, and P. Savino, “An approach to content-based image retrieval based on the Lucene search engine library”. In: Proceedings of the 14th European conference on research and advanced technology for digital libraries, ECDL’10. Springer, Berlin, pp 55–66
10) S.D. Gollapalli, P. Mitra, and C.L. Giles, “Ranking authors in digital libraries”. In: JCDL, pp. 251–254. ACM, New York (2011)
11) M. Grineva, M. Grinev, and D. Lizorkin, “Extracting key terms from noisy and multi theme documents”, Proceedings of the 18th international conference on World wide web, April 20-24, 2009, Madrid, Spain
12) J. Kang, X. Du, T. Liu, and H. Hu, “Automatic domain terminology extraction using graph mutual reinforcement,” in Proceedings of the 11th international conference on Web-age information management (WAIM 2010), July 2010.
13) J. Ma, W. Xu, Y.-H. Sun, E. Turban, S. Wang, and O. Liu, "An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection," Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on , vol.42, no.3, pp.784-790, May 2012
14) D.L. Lee, C. Huei, and K. Seamons, "Document ranking and the vector-space model," Software, IEEE , vol.14, no.2, pp.67-75, Mar/Apr 1997
15) P. Liu, K. Liu and J. liu, “Ontology based Expertise Matching System within Academia” IEEE Conference on Wireless Communications, Networking and Mobile Computing, 2007.
16) C.-Y. Lu, S.-W. Ho, J.-M. Chung, F.-Y. Hsu, H.-M. Lee, and J-M Ho, "Mining Fuzzy Domain Ontology Based on Concept Vector from Wikipedia Category Network," Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on , vol.3, no., pp.249-252, 22-27 Aug. 2011
17) D. McClosky, E. Charniak , and M. Johnson, “Automatic domain adaptation for parsing, Human Language Technologies”: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, p.28-36, June 02-04, 2010, Los Angeles, California
18) B. Milosavljevic, Danijela Boberic, and D. Surla, "Retrieval of bibliographic records using Apache Lucene", Electronic Library, The, Vol. 28 Iss: 4, pp.525 – 539, 2010
19) D. Mimno and A. McCallum. “Expertise modeling for matching papers with reviewers”. In KDD ’07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 500–509, New York, NY, USA, 2007. ACM.
20) C. Moreira, P. Calado, and B. Martins, “Learning to Rank for Expert Search in Digital Libraries of Academic Publications”, Progress in Artificial Intelligence, Springer Berlin / Heidelberg, LNAI 7026, pp. 431–445, 2011
21) H. Nakagawa, and T. Mori, “A simple but powerful automatic term extraction method”, COLING-02 on COMPUTERM 2002: second international workshop on computational terminology, p.1-7, August 31, 2002
22) V. V. Raghavan, and S.K.M. Wong, “A critical analysis of vector space model for information retrieval”. Journal of the American Society for Information Science, 37, 279–287, 1986
23) E. Smirnova, K. Balog, P. Clough et al., “A User-Oriented Model for Expert Finding”, : ECIR 2011, LNCS 6611, pp. 580–592, 2011.c Springer-Verlag Berlin Heidelberg 2011.
24) Y.-H. Sun, J. Ma, Z.-P. Fan, and J. Wang, “A hybrid knowledge and model approach for reviewer assignment”, Expert Systems with Applications, Volume 34, Issue 2, February 2008, Pages 817-824, ISSN 0957-4174
25) G. Tsatsaronis, I. Varlamis, K. Norv°ag, and M. Vazirgiannis, “Omiotis: A Thesaurus-Based Measure of Text Relatedness”. W. Buntine et al. (Eds.): ECML PKDD 2009, Part II, LNAI 5782, pp. 742–745, 2009. Springer-Verlag Berlin Heidelberg 2009

26) G. Tsatsaronis, I. Varlamis, and M. Vazirgiannis, "Text Relatedness Based on a Word Thesaurus", Journal of Artificial Intelligence Research , Volume 37 Issue 1, January 2010
27) G. Tsatsaronis , and Vicky Panagiotopoulou, “A generalized vector space model for text retrieval based on semantic relatedness”, Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, p.70-78, April 02-02, 2009, Athens, Greece
28) R. W. White , S. T. Dumais , and J. Teevan, “Characterizing the influence of domain expertise on web search behavior”, Proceedings of the Second ACM International Conference on Web Search and Data Mining, February 09-12, 2009, Barcelona, Spain
29) C.-J. Wu, J.-M. Chung, C.-Y. Lu, H.-M. Lee, and J.-M. Ho, "Using Web-Mining for Academic Measurement and Scholar Recommendation in Expert Finding System," wi-iat, vol. 1, pp.288-291, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011
30) K.-H. Yang, C.-Y. Chen, H.-M. Lee and J.-M. Ho, "EFS: Expert Finding System Based on Wikipedia Link Pattern Analysis," the 2008 IEEE International Conference on Systems, Man and Cybernetics (SMC 2008), 2008.
31) K.-H. Yang, T.-L. Kuo, Lee, H.-M. Lee and J.-M. Ho, "A Reviewer Recommendation System Based on Collaborative Intelligence," Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT '09. IEEE/WIC/ACM International Joint Conferences, vol.1, no., pp.564-567, 15-18 Sept. 2009.
32) M. Zhang, X. Lin, X. Dai, X. Wu, "Parsing-based automatic Chinese term extraction," Natural Language Processing and Knowledge Engineering (NLP-KE), 2011 7th International Conference on , vol., no., pp.122-125, 27-29 Nov. 2011