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bX Article
Recommender

Enrich your
discovery
experience

bX captures anonymous usage information from millions of scholars around the world, then leverages this data to enrich and expand the user discovery experience with relevant recommendations for articles and ebooks. Starting from an article of interest, bX provides users with other relevant articles for the same topic. While the initial article serves as an entry point, the recommended material can provide new inspiration for learning and broaden the scope of research, going beyond the initial search query. Depending on their nature, recommendations can help narrow or widen learning topics, provide new keywords to describe the topic, and allow the user to find items by chance through serendipitous discovery.

How does bX work?

bX harvests link resolver usage data from many academic institutions around the world. If two articles are used in the same session, the system analyzes the connection between them and stores the items in a co-retrieval network. Because bX recommendations are based on link resolver usage, they are truly platform- and content-neutral. The usage is generated through discovery systems, A&I databases, publisher platforms, and any other source that links users to full text via a link resolver. The articles may be from different journals, publishers, and platforms.

Where can I find bX Recommendations?

bX is a subscription service that is embedded into Primo and Summon discovery services, the SFX and Alma link resolver interfaces, and the Leganto reading list solution. APIs are available to easily embed the service and its article recommendations into other interfaces.

bX origins

bX is based on research that was conducted at Los Alamos National Lab by Herbert Van de Sompel and Johan Bollen. Herbert Van de Sompel is the 2017 recipient of the Paul Evan Peters Award, awarded by CNI, Coalition for Networked Information. He was also the original inventor of the SFX link resolver and the OpenURL framework, in collaboration with Ex Libris.