Personalized queries under a generalized preference model

Abstract

Query personalization is the process of dynamically enhancing a query with related user preferences stored in a user profile with the aim of providing personalized answers. The underlying idea is that different users may find different things relevant to a search due to different preferences. Essential ingredients of query personalization are: (a) a model for representing and storing preferences in user profiles, and (b) algorithms for the generation of personalized answers using stored preferences. Modeling the plethora of preference types is a challenge. In this paper, we present a preference model that combines expressivity and concision. In addition, we provide efficient algorithms for the selection of preferences related to a query, and an algorithm for the progressive generation of personalized results, which are ranked based on user interest. Several classes of ranking functions are provided for this purpose. We present results of experiments both synthetic and with real users (a) demonstrating the efficiency of our algorithms, (b) showing the benefits of query personalization, and (c) providing insight as to the appropriateness of the proposed ranking functions.