Identifying related Documents for Research Paper Recommender by CPA and COA

摘要

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.