Legal research recommendation system

A method for improving a computerized search system's research recommendations, embodied as instructions stored in non-transitory computer memory of the computerized search system which, when executed by a computer processor, are configured to:

• receive, using an application programming interface (API), a first binary encoded signal associated with an input text provided by a user computing device;
• parse the input text to identify one or more conceptual issue topics relevant thereto, including at least identifying any cites to other documents contained in the input text and identifying conceptual issue topics in the input text based on any conceptual issue topics known to be relevant to the identified cited documents;
• identify recommendation candidates known to be relevant to the one or more identified conceptual issue topics;
• and
• transmit, using the API, a second binary encoded signal to the user computing device, the second binary encoded signal being configured to render results of the identifying step via a user interface of the user computing device, the results comprising the recommendation candidates ranked according to a respective relevancy score indicating how relevant each recommendation candidate is to the input text, wherein the relevancy score is based on a weighted aggregation of at least (i) a legal jurisdiction score, (ii) an authority score, and (iii) a recency score, and (iv) a recommendation-frequency score, wherein the legal jurisdiction score indicates a closeness of legal jurisdiction scope between the recommendation candidate and the input text based on a comparison between a first legal jurisdiction code associated with the recommendation candidate and a second legal jurisdiction code associated with the input text, wherein the authority score indicates a degree of authoritativeness of the recommendation candidate with respect to one or more other recommendation candidates, wherein the recency score indicates a recentness of the recommendation candidates determined based on document metadata extracted from an electronic record database, and wherein the recommendation-frequency score indicates a degree of similarity between headnotes relevant to the input text and headnotes identified in data included in respective headnote portions of the recommendation candidates.