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
