User Models for Adaptive Information Retrieval on the Web

Searching information can be realized thanks to specific tools called Information Retrieval Systems IRS (also called “search engines”). To provide more accurate results to users, most of such systems offer personalization features. To do this, each system models a user in order to adapt search results that will be displayed. In a multi-application context (e.g., when using several search engines for a unique query), personalization techniques can be considered as limited because the user model (also called profile) is incomplete since it does not exploit actions/queries coming from other search engines. So, sharing user models between several search engines is a challenge in order to provide more efficient personalization techniques. A semantic architecture for user profile interoperability is proposed to reach this goal. This architecture is also important because it can be used in many other contexts to share various resources models, for instance a document model, between applications. It is also ensuring the possibility for every system to keep its own representation of each resource while providing a solution to easily share it.

Adaptive Information Retrieval Based on Task Context

This chapter discusses using context in Information Retrieval systems and Intelligent Assistant Agents in order to improve the performance of these systems. The notion of context is introduced and the state of the art in Contextual Information Retrieval is presented which illustrates various categories of contexts that can be taken into account when solving user queries. In this framework, the authors focus on the issue of task-based context which takes into account the current activity the user is involved in when he puts a query. Finally they introduce promising research directions that promote the use of Intelligent Assistant Agents capable of symbolic reasoning about users’ tasks for supporting the query process.