Preprint of: Bela Gipp and Joran Beel. Identifying Related Documents For Research Paper Recommender By CPA And COA. In S. I. Ao, C. Douglas, w.S. Grundfest, and J. Burgstone, editors, Intemational Conference on Education and Information Technology (ICElTo9), volume I of Lecture Notes in Engineering and Computer Science, pages 636-639, Berkeley (USA), October 2009. International Association of Engineers(LAENG), Newswood Limited Isbn978-988-17012-6-8.Downloadedfromhttp://www.sciploreorg Identifying Related Documents For Research Paper Recommender by cpa and coa Bela gipp and Joran Beel Otto-von-Guericke University Magdeburg, Department of Computer Science, ITI and SciPlore. org gipplbeel@sciplore. org Abstrack-This work-in-progress paper introduces two new results can be achieved by applying co-citation analysis pproaches called Citation Proximity Analysis( CPA)and Citation proximity analysis is a further development of co- Citation Order Analysis (COA). They can be applied to citation analysis identify related documents for the purpose of research paper recommender systems. CPA is a variant of co-citation analysis hat additionally considers the proximity of citations to each ther within an article's full-text. The underlying idea is that the closer citations are to each other in a document, the more 2 and a e likely it is that the cited documents are related. For example 如mM itations listed in the same sentence are more likely to express related thoughts than citations listed only in the same section. In COA, the order of citations are considered, allowing the identification of a text similar to one that has been translated from language A to language b, as the citations would still ccur in the same order. However, it is also shown that CPa and COA cannot replace text analysis and existing citation alysis approaches for research paper recommender syste since they all have their own strengths and weaknesses. 巴四““黨 Index Terms-Bibliometrics, citation proximity analysis, citation order analysis, related documents, research paper Figure 1: GUI SciPlore- clustering similar documents recommender In the research paper recommender SciPlore. org this approach is mainly used for two purposes. First, to cluster . INTRODUCTION similar documents as shown in Figure 1; and secondly, to give recommendations for further related documents based The search for related work is a time-consuming procedure on one or more documents the user has been interested in, that even if performed by experienced scientists often leads to unsatisfying results. To alleviate the problem, search engines such as Google Scholar and Citeseer offer to In the first part of this paper related work is presented ane display "similar"documents based on text and citation the commonly applied citation analysis approaches discussed with the focus on co-citation analysis. In the Superior results are usually achieved by hybrid research following section the CPA approach is introduced paper recommender systems. By combining further Afterwards, the existing citation analysis approaches are techniques such as co-word analysis, collaborative filtering. compared to CPa and their suitability for research paper Subject-Action-Object (SAO)structures, etc, more precis systems examined. The paper concludes with a summary and an outlook which includes how this new approach is commendations can be given. However, these approaches are only suitable to a limited extent for identifying related going to be integrated in the research paper recommender SciPlore or work [2-81 Taking everything into account, our examination suggests that in the case of scientific documents, usually the bestIdentifying Related Documents For Research Paper Recommender By CPA and COA Bela Gipp and Jöran Beel Otto-von-Guericke University Magdeburg, Department of Computer Science, ITI and SciPlore.org gipp|beel@sciplore.org Abstract—This work-in-progress paper introduces two new approaches called Citation Proximity Analysis (CPA) and Citation Order Analysis (COA). They can be applied to identify related documents for the purpose of research paper recommender systems. CPA is a variant of co-citation analysis that additionally considers the proximity of citations to each other within an article’s full-text. The underlying idea is that the closer citations are to each other in a document, the more likely it is that the cited documents are related. For example, citations listed in the same sentence are more likely to express related thoughts than citations listed only in the same section. In COA, the order of citations are considered, allowing the identification of a text similar to one that has been translated from language A to language B, as the citations would still occur in the same order. However, it is also shown that CPA and COA cannot replace text analysis and existing citation analysis approaches for research paper recommender systems since they all have their own strengths and weaknesses. Index Terms—Bibliometrics, citation proximity analysis, citation order analysis, related documents, research paper recommender I. INTRODUCTION The search for related work is a time-consuming procedure that even if performed by experienced scientists often leads to unsatisfying results. To alleviate the problem, search engines such as Google Scholar and Citeseer offer to display “similar” documents based on text and citation analysis. Superior results are usually achieved by hybrid research paper recommender systems. By combining further techniques such as co-word analysis, collaborative filtering, Subject-Action-Object (SAO) structures, etc., more precise recommendations can be given. However, these approaches are only suitable to a limited extent for identifying related work [2-8]. Taking everything into account, our examination suggests that in the case of scientific documents, usually the best results can be achieved by applying co-citation analysis. Citation proximity analysis is a further development of co￾citation analysis. CCoocckkppiitt VViieeww Server connection with Scienstein.org established Data processing completed Graphical View (relevant documents are larger) Filter Publication date between: 2002 and 2008 Impact factor: Relevance: 2.5 7.5 Publication types Select languages Collaborative rating: 3.2 Change Query 2002 2003 2004 2005 2006 2007 2008 Settings Topicality Legend 2.5 Unrat 0-2 2-4 4-6 6-8 Collaborative R. Year Impact Year 8-10 Content Based Recommender Systems Evaluating Collaborative Recommender Systems JL Herlocker, JA Konstan, G Terveen and JT Riedl 2006, Journal of Science and Recommenders (IF 3.2) Abstract: Recommender systems have been evaluated in many, often incomparable, ways. In this paper we review the key decisions in evaluating collaborative filtering recommender systems… More Tags: Recommender Systems Collaboration Evaluation Metrics Performance Measurement 23 Data Mining Collaborative Document Evaluation Recommender Systems Figure 1: GUI SciPlore – clustering similar documents In the research paper recommender SciPlore.org this approach is mainly used for two purposes. First, to cluster similar documents as shown in Figure 1; and secondly, to give recommendations for further related documents based on one or more documents the user has been interested in, as shown in Figure 2. In the first part of this paper related work is presented and the commonly applied citation analysis approaches discussed with the focus on co-citation analysis. In the following section the CPA approach is introduced. Afterwards, the existing citation analysis approaches are compared to CPA and their suitability for research paper systems examined. The paper concludes with a summary and an outlook which includes how this new approach is going to be integrated in the research paper recommender SciPlore.org. Preprint of: Bela Gipp and Jöran Beel. Identifying Related Documents For Research Paper Recommender By CPA And COA. In S. I. Ao, C. Douglas, W. S. Grundfest, and J. Burgstone, editors, International Conference on Education and Information Technology (ICEIT'09), volume 1 of Lecture Notes in Engineering and Computer Science, pages 636–639, Berkeley (USA), October 2009. International Association of Engineers (IAENG), Newswood Limited. ISBN 978-988-17012-6-8. Downloaded from http://www.sciplore.org