Can we do better than co-citations? Bringing Citation Proximity Analysis from idea to practice in research articles recommendation

Knoth, Petr and Khadka, Anita (2017). Can we do better than co-citations? Bringing Citation Proximity Analysis from idea to practice in research articles recommendation. In: Proceedings of the 2nd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017) (Mayr, Philipp; Chandrasekaran, Muthu Kumar and Jaidka, Kokil eds.), pp. 14–25.

URL: http://ceur-ws.org/Vol-1888/paper2.pdf

Abstract

In this paper, we build on the idea of Citation Proximity Analysis (CPA), originally introduced in [1], by developing a step by step scalable approach for building CPA-based recommender systems. As part of this approach, we introduce three new proximity functions, extending the basic assumption of co-citation analysis (stating that the more often two articles are co-cited in a document, the more likely they are related) to take the distance between the co-cited documents into account. Ask- ing the question of whether CPA can outperform co-citation analysis in recommender systems, we have built a CPA based recommender system from a corpus of 368,385 full-texts articles and conducted a user survey to perform an initial evaluation. Two of our three proximity functions used within CPA outperform co-citations on our evaluation dataset.

Viewing alternatives

Look up in Google Scholar

Download history

Item Actions

Export

About

CORE (COnnecting REpositories)

Recommendations