This article outlines an approach that avoids these problems by choosing a retrieval method that utilizes minimal repository structure to effectively support the process of finding software components. The approach is demonstrated through a pair of proof-ofconcept prototypes: PEEL, a tool to semiautomatically identify reusable components, and CodeFinder, a retrieval system that compensates for the lack of explicit knowledge structures through a spreading activation retrieval process. CodeFinder also allows component representations to be modified while users are searching for information. This mechanism adapts to the changing nature of the information in the repository and incrementally improves the repository while people use it. The combination of these techniques holds potential for designing software repositories that minimize up-front costs, effectively support the search process, and evolve with an organization's changing needs.

We describe results from Web search log studies aimed at elucidating user behaviors associated with queries and destination URLs that appear with different frequencies. We note the diversity of information goals that searchers have and the differing ways that goals are specified. We examine rare and common information goals that are specified using rare or common queries. We identify several significant differences in user behavior depending on the rarity of the query and the destination URL. We find that searchers are more likely to be successful when the frequencies of the query and destination URL are similar. We also establish that the behavioral differences observed for queries and goals of varying rarity persist even after accounting for potential confounding variables, including query length, search engine ranking, session duration, and task difficulty. Finally, using an information-theoretic measure of search difficulty, we show that the benefits obtained by search and navigation actions depend on the frequency of the information goal.

We present a user modeling system for personalized interaction and tailored retrieval that (1) tracks interactions over time, (2) represents multiple information needs, both short and long term, (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 describes how information search and use behavior can be used to identify and track information needs. The personal behavioral model characterizes 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 seeking 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.