Parity Computing Announces Industry Leading Journal Recommendation System

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New software helps researchers target their manuscripts

Parity Computing today announced the launch of Parity Journal Sense™, a powerful new recommendation system for helping science, technology, and medical (STM) researchers identify the most suitable journal for publishing their manuscripts.

Journal Sense users submit keywords, an abstract, or the entire text of their unpublished manuscript, and obtain a ranked list of journal recommendations. Journal Sense achieves industry leading relevance accuracy, which is significantly higher than simpler keyword matching solutions, and can often recommend relevant journals that authors may not be familiar with or did not realize would be suitable for their research.

“The accuracy of our system has been evaluated by third-parties via both automated roll-back-the-clock tests and manual expert assessments”, said Dr. Mark Land, Parity’s Chief Scientist for STM Analytics. “A simple way to summarize the results is that Journal Sense is roughly four times as accurate as straightforward keyword matching, and is also significantly more accurate than any other available system for which accuracy numbers have been reported”.

Journal Sense is built on Parity’s Semantic Profiling Engine™, which quantitatively characterizes (profiles) both the manuscript and the candidate set of journals. “We use a carefully optimized mix of features, term normalization, and a proprietary Parity weighting function to generate term vectors for both queries and journals, and then compute match scores”, said Dr. Aditya Sehgal, Parity’s Director of Research and New Products.

The Journal Sense recommendation engine is deployed for customers as a Software-as-a-Service (SaaS) application, and interfaces easily with any front end system. The SaaS offering is accompanied by Parity’s index of the MEDLINE corpus, and can also be integrated with customer-owned content upon request.

About Parity Computing
Parity provides powerful data mining, analytics, and decision-support systems to publishers and enterprises in the science, technology, and medical (STM) industry.

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Prameela Tudor
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