The Role-Relevance Model for Enhanced Semantic Targeting in Unstructured Text
Abstract
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). Topical relevance is assessed using a novel extension to Latent Dirichlet Allocation (LDA) that allows standard LDA to score document relevance to user-defined topics. Overall results on a pre-labeled corpus show an average improvement in search precision of approximately 20% compared to keyword search alone.
Cite
@article{arxiv.1804.07447,
title = {The Role-Relevance Model for Enhanced Semantic Targeting in Unstructured Text},
author = {Christopher A. George and Onur Ozdemir and Connie Fournelle and Kendra E. Moore},
journal= {arXiv preprint arXiv:1804.07447},
year = {2018}
}
Comments
10 pages, 3 figures, 6 tables, presented at SPIE Defense + Commercial Sensing: Next Generation Analyst (2018)