Finding Your Literature Match -- A Recommender System
Edwin A. Henneken, Michael J. Kurtz, Alberto Accomazzi, Carolyn Grant,, Donna Thompson, Elizabeth Bohlen, Giovanni Di Milia, Jay Luker, Stephen S., Murray

TL;DR
This paper introduces a recommender system designed to help users find relevant literature efficiently amidst the growing and interdisciplinary body of scholarly work, enhancing search experiences.
Contribution
It proposes a novel recommendation approach that can be integrated into existing information retrieval systems to improve literature discovery.
Findings
Demonstrates the feasibility of integrating recommendations into search systems.
Shows potential for increased user engagement and satisfaction.
Addresses the challenge of information overload in scholarly searches.
Abstract
The universe of potentially interesting, searchable literature is expanding continuously. Besides the normal expansion, there is an additional influx of literature because of interdisciplinary boundaries becoming more and more diffuse. Hence, the need for accurate, efficient and intelligent search tools is bigger than ever. Even with a sophisticated search engine, looking for information can still result in overwhelming results. An overload of information has the intrinsic danger of scaring visitors away, and any organization, for-profit or not-for-profit, in the business of providing scholarly information wants to capture and keep the attention of its target audience. Publishers and search engine engineers alike will benefit from a service that is able to provide visitors with recommendations that closely meet their interests. Providing visitors with special deals, new options and…
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