Publicación: IMPROVING PRECISION IN IR CONSIDERING DYNAMIC ENVIRONMENTS
Fecha
2019
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IIWAS2019: ACTAS DE LA 21ª CONFERENCIA INTERNACIONAL SOBRE INTEGRACIóN DE LA INFORMACIóN Y APLICACIONES Y SERVICIOS BASADOS EN LA WEB
Resumen
MUCH OF THE RESEARCH IN INFORMATION RETRIEVAL (IR) IS DEVOTED TO STUDYING THE IMPROVEMENT OF PERSONALIZED RESULTS FOR SPECIFIC USERS IN A STATIC ENVIRONMENT. NEVERTHELESS, FEW APPROACHES TAKE ADVANTAGE OF COLLECTIVE PAST SEARCHES IN A DYNAMIC CONTEXT WHERE THE NUMBER OF DOCUMENTS IS INCREASED ACCORDING WITH THE PASSAGE OF TIME. IN THIS PAPER, WE PRESENT AN ON-LINE PROBABILISTIC ALGORITHM, WHICH USES THE COLLECTIVE PAST SEARCHES IN A DYNAMIC CONTEXT TO ANSWER STATIC AND DYNAMIC QUERIES. SEVERAL EXPERIMENTS WERE CARRIED OUT WITH THE AIM OF EVALUATING THE EFFECTIVENESS OF OUR ALGORITHM. THE ALGORITHM S RESULTS WERE COMPARED WITH THE COSINE MEASURE. FOLLOWING THE CRANFIELD PARADIGM, SIMULATED DATASETS WERE USED IN THE EXPERIMENTS. FINAL RESULTS SHOW THAT IT IS POSSIBLE TO IMPROVE EFFECTIVENESS IN A DYNAMIC CONTEXT.
