Conversational Query Revision with a Finite User Profiles Model

Italian Information Retrieval Workshop 2012.

图书情报档案学;计算机科学

Henry Blanco1 ; 2 ; Francesco Ricci1 ; Derek Bridge3 1 Faculty of Computer Science ; 2 Center of Medical Biophysics

Others  :  http://ceur-ws.org/Vol-835/paper10.pdf
PID  :  41383

来源: CEUR

【 摘 要 】

Information Recommendation is a conversational approach aimed at suggesting to the user how to reformulate his queries to a product catalogue in order to find the products that maximize his utility. In previous work, it was shown that, by observing the queries selected by the user among those suggested, the system can make inferences on the true user utility function and eliminate from the set of suggested queries those retrieving products with an inferior utility (dominated queries). The computation of the dominated queries was based on the solution of several linear programming problems, which represented a major com- putational bottleneck for the eciency of the proposed solution. In this paper we propose a new technique for the computation of the dominated queries. It relies on the assumption that the set of possible user utility functions is finite. We show that under this assumption the computation of the query suggestions is simplified and the number of query suggestions is strongly reduced.