Finding Your Literature Match -- A Recommender System

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 highlights may be interesting to a certain degree, but what makes more sense (especially from a commercial point of view) than to let visitors do most of the work by the mere action of making choices? Hiring psychics is not an option, so a technological solution is needed to recommend items that a visitor is likely to be looking for. In this presentation we will introduce such a solution and argue that it is practically feasible to incorporate this approach into a useful addition to any information retrieval system with enough usage.