Virtual Citation Proximity (VCP): Learning a Hypothetical In-Text Citation-Proximity Metric For Uncited Documents

Abstract

The relatedness of research articles, patents, court rulings, webpages, and other document types is often calculated with citation or hyperlink-based approaches like co-citation (proximity) analysis. The main limitation of citation-based approaches is that they cannot be used for documents that receive little or no citations. We propose Virtual Citation Proximity (VCP), a Siamese Neural Network architecture, which combines the advantages of co-citation proximity analysis (diverse notions of relatedness / high recommendation performance), with the advantage of content-based filtering (high coverage). VCP is trained on a corpus of documents with textual features, and with real citation proximity as ground truth. VCP then predicts for any two documents, based on their title and abstract, in what proximity the two documents would be co-cited, if they were indeed co-cited. The prediction can be used in the same way as real citation proximity to calculate document relatedness, even for uncited documents. In our evaluation with 2 million co-citations from Wikipedia articles, VCP achieves an MAE of 0.0055, i.e. an improvement of 20% over the baseline, though the learning curve suggests that more work is needed.

A Qualitative Evaluation of User Preference for Link-based vs. Text-based Recommendations of Wikipedia Articles

“…Recent advancements in recommender system research are focused on neural-based approaches, which may lead to the belief that the examined methods, MLT and CPA, are dated. This, however, is not the case since they are still used in practice, as Wikipedia's MLT deployment shows (Section 2.2), and the intuition of CPA is the basis for neural approaches like in Virtual Citation Proximity [30].…”

“…Despite the large size and popularity of Wikipedia, the potential of this corpus for evaluating LRS has thus far not been exploited by the research community. To the best of our knowledge, no prior work, aside from the initial offline study [35] and the work by [30], has made use of the Wikipedia corpus to evaluate the effectiveness of different recommendation approaches. The implications of studying the user-perceived recommendation effectiveness for Wikipedia articles may also be applicable to Wikimedia projects in a broader context, which tend to contain a high frequency of links.…”