Personalized search provides a potentially powerful tool, however, it is limited due to the large number of roles that a person has: parent, employee, consumer, etc. We present the role-relevance algorithm: a search technique that favors search results relevant to the user’s current role. The role-relevance algorithm uses three factors to score documents: (1) the number of keywords each document contains; (2) each document’s geographic relevance to the user’s role (if applicable); and (3) each document’s topical relevance to the user’s role (if applicable). Results on a pre-labeled corpus show an average improvement in search precision of approximately 20% compared to keyword search alone. We further consider several extensions to this algorithm.
Christopher A. George, Onur Ozdemir, Connie E. Fournelle, and Kendra E. Moore, "The role-relevance model for enhanced semantic targeting in unstructured text," Proc. SPIE 10653, Next-Generation Analyst VI, 1065306 (Presented at SPIE Defense + Security: April 16, 2018; Published: 27 April 2018); https://doi.org/10.1117/12.2306513.
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