We formulate and study search algorithms that consider a user’s prior interactions with a wide variety of content to personalize that user’s current Web search. Rather than relying on the unrealistic assumption that people will precisely specify their intent when searching, we pursue techniques that leverage implicit information about the user’s interests. This information is used to re-rank Web search results within a relevance feedback framework. We explore rich models of user interests, built from both search-related information, such as previously issued queries and previously visited Web pages, and other information about the user such as documents and email the user has read and created. Our research suggests that rich representations of the user and the corpus are important for personalization, but that it is possible to approximate these representations and provide efficient client-side algorithms for personalizing search. We show that such personalization algorithms can significantly improve on current Web search.

Understanding the extent to which people’s search behaviors differ in terms of the interaction flow and information targeted is important in designing interfaces to help World Wide Web users search more effectively. In this paper we describe a longitudinal log-based study that investigated variability in people’s interaction behavior when engaged in search-related activities on the Web. We analyze the search interactions of more than two thousand volunteer users over a five-month period, with the aim of characterizing differences in their interaction styles. The findings of our study suggest that there are dramatic differences in variability in key aspects of the interaction within and between users, and within and between the search queries they submit. Our findings also suggest two classes of extreme user – navigators and explorers – whose search interaction is highly consistent or highly variable. Lessons learned from these users can inform the design of tools to support effective Web-search interactions for everyone.

Connections is a file system search tool that combines traditional content-based search with context information gathered from user activity. By tracing file system calls, Connections can identify temporal relationships between files and use them to expand and reorder traditional content search results. Doing so improves both recall (reducing falsepositives) and precision (reducing false-negatives). For example, Connections improves the average recall (from 13% to 22%) and precision (from 23 % to 29%) on the first ten results. When averaged across all recall levels, Connections improves precision from 17 % to 28%. Connections provides these benefits with only modest increases in average query time (2 seconds), indexing time (23 seconds daily), and index size (under 1 % of the user’s data set).

People often repeat Web searches, both to find new information on topics they have previously explored and to re-find information they have seen in the past. The query associated with a repeat search may differ from the initial query but can nonetheless lead to clicks on the same results. This paper explores repeat search behavior through the analysis of a one-year Web query log of 114 anonymous users and a separate controlled survey of an additional 119 volunteers. Our study demonstrates that as many as 40 % of all queries are re-finding queries. Refinding appears to be an important behavior for search engines to explicitly support, and we explore how this can be done. We demonstrate that changes to search engine results can hinder refinding, and provide a way to automatically detect repeat searches and predict repeat clicks.

Web search services are among the most heavily used applications on the World Wide Web. Perhaps because search is used in such a huge variety of tasks and contexts, the user interface must strike a careful balance to meet all user needs. We describe a study that used eye tracking methodologies to explore the effects of changes in the presentation of search results. We found that adding information to the contextual snippet significantly improved performance for informational tasks but degraded performance for navigational tasks. We discuss possible reasons for this difference and the design implications for better presentation of search results. Author Keywords Web search, eye tracking, contextual snippets, user studies.

We posit that a semistructured data model o#ers the right balance of rich structure and flexible (or lack of) schema allowing naive end users to record information in whatever form makes it easy for them to manage. We describe our Haystack system, which exposes the richness and flexibility of the data model while o#ering the user natural, traditional interfaces that shield them from the specifics of schemas, tuples, and database queries. We outline research challenges that remain to be addressed.

With the growing importance of systems containing arbitrary semistructured relationships, the need for supporting users searching in such repositories has grown. Currently support for users' search needs either has required domain-specific user interfaces or has required users to be schema experts. We have developed a generalpurpose tool that offers users helpful navigation and refinement options for seeking information in these semistructured repositories. We show how a tool can be built without requiring domain-specific assumptions about the information being explored. In addition to describing a general approach to the problem, we provide a set of natural, general-purpose refinement tactics, many generalized from past work on textual information retrieval.

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.

† HP Labs As the scope of personal data grows, it becomes increasingly difficult to find what we need when we need it. Desktop search tools provide a potential answer, but most existing tools are incomplete solutions: they index content, but fail to capture dynamic relationships from the user’s context. One emerging solution to this is contextenhanced search, a technique that reorders and extends the results of content-only search using contextual information. Within this framework, we propose using strict causality, rather than temporal locality, the current state of the art, to direct contextual searches. Causality more accurately identifies data flow between files, reducing the false-positives created by context-switching and background noise. Further, unlike previous work, we conduct an online user study with a fully-functioning implementation to evaluate user-perceived search quality directly. Search results generated by our causality mechanism are rated a statistically-significant 17 % higher on average over all queries than by using content-only search or context-enhanced search with temporal locality. 1

We investigate the diverse goals people have when they issue the same query to a Web search engine, and the ability of current search tools to address such diversity, in order to understand the potential value of personalizing search results. Great variance was found in the results different individuals rated as relevant for the same query---even when those users expressed their underlying informational goal in the same way. The analysis suggests that, while current Web search tools do a good job of retrieving results to satisfy the range of intentions people may associate with a query, they do not do a very good job of discerning an individual's unique search goal. We discuss the implications of this study on the design of search systems and suggest areas for additional research.