A User Modeling System for Personalized Interaction and Tailored Retrieval in Interactive IR Diane Kelly zyxwvutsrq School zyxwvutsrq of Communication, Information and Library Studies, Rutgers University, 4 Huntington St., New Brunswick, NJ 08901. E-mail: diane@scils.rutgers.edu Nicholas J. Belkin School of Communication, Information and Library Studies, Rutgers University, 4 Huntington St., New Brunswick, NJ 08901. We present a user modeling system for personal- ized interaction and tailored retrieval that (1) tracks interactions over time, (2) represents mul- tiple information needs, both short and long term, zyxwvu (3) allows for changes in information needs over time, (4) acquires and updates the user model automatically, without explicit assistance from the user, and (5) accounts for contextual factors such as topic familiarity and endurance of need. The proposed system contains three major classes of models: general behavioral, personal behavioral and topical. The general behavioral model de- scribes how information search and use behavior can be used to identify and track information needs. The personal behavioral model character- izes an individual user’s information search and use behavior with regard to document preference and states of knowledge. Finally, the topical model characterizes the user’s information seek- ing needs. We describe how such a model can be used to personalize search interactions and tailor system responses to individuals across multiple information seeking sessions. Introduction As information becomes increasingly available, users are faced with an overabundance of sources in which they must choose. Collections are no longer homogeneous sets of documents with conventional structure and standard vo- cabulary. but are instead, heterogeneous sets of documents with varying structure and undifferentiated vocabularies. Not only is information increasingly available, but it is also increasingly accessible. As more information is distributed electronically, a user’s information seeking activities are no longer bound geographically or temporally. Now, more than ever, it is critical for systems to obtain a more accurate representation of the user’s information needs, document preferences and states of knowledge and to maintain these representations over time. Most IR systems assume that information seeking epi- sodes are discrete and unrelated. Recent work (Lin & Bel- kin, 2000; Spink, 1996; Spink, Griesdorf & Bateman, 1999) has challenged this assumption and provided evidence that information seeking typically occurs across multiple search sessions and that information needs often evolve throughout the process of successive searching. Traditional IR systems have construed searching to occur within a single search session. Search sessions and users are synonymous terms; for each search session, the system assumes a new user. Because of this, much information about the user and the user’s preferences and states of knowledge is lost. Some systems may provide the illusion of continuity, such as a query history or saved documents lists, but this information only acts as a memory aid or navigation feature. The system does not use this information to characterize the user and the user’s preferences or to aid in the retrieval of docu- ments. Each time the user initiates searching, hehhe must begin anew as far as the IR system is concerned. Even though the user’s information seeking activities can occur during multiple search sessions across multiple time peri- ods, the system views these activities as occurring within specific instances of time. Essentially, the system knows nothing about the user and is unable to determine that the user searching currently for information about “The Flea” is the same user that searched for information about the poetry of John Donne just four days ago. To know even this small bit of information would make the difference between re- turning documents about flea control and flea markets and returning documents about the poem. User Modeling User modeling (UM) offers the potential of individuating users and tracking the information seeking behavior and information needs of the user over time. Generally speak- ing, a user model is a description of a user, created or se- zy ASIST 2002 Contributed zyxwvutsrq Paper 3 16