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.

Member-maintained communities ask their users to perform tasks the community needs. From Slashdot, to IMDb, to Wikipedia, groups with diverse interests create communitymaintained artifacts of lasting value (CALV) that support the group’s main purpose and provide value to others. Said communities don’t help members find work to do, or do so without regard to individual preferences, such as Slashdot assigning meta-moderation randomly. Yet social science theory suggests that reducing the cost and increasing the personal value of contribution would motivate members to participate more. We present SuggestBot, software that performs intelligent task routing (matching people with tasks) in Wikipedia. SuggestBot uses broadly applicable strategies of text analysis, collaborative filtering, and hyperlink following to recommend tasks. SuggestBot’s intelligent task routing increases the number of edits by roughly four times compared to suggesting random articles. Our contributions are: 1) demonstrating the value of intelligent task routing in a real deployment; 2) showing how to do intelligent task routing; and 3) sharing our experience of deploying a tool in Wikipedia, which offered both challenges and opportunities for research.

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Major Web search engines, such as AltaVista, are essential tools in the quest to locate online information. This article reports research that used transaction log analysis to examine the characteristics and changes in AltaVista Web searching that occurred from 1998 to 2002. The research questions we examined are (1) What are the changes in AltaVista Web searching from 1998 to 2002? (2) What are the current characteristics of AltaVista searching, including the duration and frequency of search sessions? (3) What changes in the information needs of AltaVista users occurred between 1998 and 2002? The results of our research show (1) a move toward more interactivity with increases in session and query length, (2) with 70 % of session durations at 5 minutes or less, the frequency of interaction is increasing, but it is happening very quickly, and (3) a broadening range of Web searchers ’ information needs, with the most frequent terms accounting for less than 1 % of total term usage. We discuss the implications of these findings for the development of Web search engines.

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.

One way to help all users of commercial Web search engines be more successful in their searches is to better understand what those users with greater search expertise are doing, and use this knowledge to benefit everyone. In this paper we study the interaction logs of advanced search engine users (and those not so advanced) to better understand how these user groups search. The results show that there are marked differences in the queries, result clicks, post-query browsing, and search success of users we classify as advanced (based on their use of query operators), relative to those classified as non-advanced. Our findings have implications for how advanced users should be supported during their searches, and how their interactions could be used to help searchers of all experience levels find more relevant information and learn improved searching strategies. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: query formulation, search process, relevance feedback.

Conventional wisdom holds that queries to information retrieval systems will yield more relevant results if they contain multiple topic-related terms and use Boolean and phrase operators to enhance interpretation. AlthoughstudieshaveshownthattheusersofWeb-based searchenginestypicallyentershort,term-basedqueries and rarely use search operators, little information exists concerning the effects of term and operator usage on the relevancy of search results. In this study, search engine users formulated queries on eight search topics. Each query was submitted to the user-specified search engine, and relevancy ratings for the retrieved pages were assigned. Expert-formulated queries were also submittedandprovidedabasisforcomparingrelevancy ratings across search engines. Data analysis based on our research model of the term and operator factors affecting relevancy was then conducted. The results showthatthedifferenceinthenumberoftermsbetween expert and nonexpert searches, the percentage of matching terms between those searches, and the erroneous use of nonsupported operators in nonexpert searchesexplainmostofthevariationintherelevancyof search results. These findings highlight the need for designing search engine interfaces that provide greater support in the areas of term selection and operator usage.

Search engine logs not only keep navigation information, but also the queries made by their users. In particular, queries to a search engine follow a power-law distribution, which is far from uniform. Queries and related clicks can be used to improve the search engine itself in different aspects: user interface, index performance, and answer ranking. In this chapter we present some of the main ideas proposed in query mining and we show a few examples based on real data from a search engine focused in the Chilean Web. 1

While researchers have been studying user activity on the Web since its inception, there remains a lack of understanding of the high level tasks in which users engage on the Web. We have recently conducted a field study in which participants were asked to annotate all web usage with a task description and categorization. Based on our analysis of participants’ recorded tasks during the field study, as well as previous research, we have developed a goalbased classification of information tasks which describes user activities on the Web.

Although search engines are playing an increasingly important role in users ’ Web access, our understanding is still limited regarding the magnitude of search-engine influence. For example, how many times do people start browsing the Web from a search engine? How much percentage of Web traffic is incurred as a result of search? To what extent does a search engine like Google extend the scope of Websites that users can reach? To study these issues, in this paper we analyze a real Web access trace collected over a period of two and half months from the UCLA Computer Science Department. Our study indicates that search engines influence about 13.6 % of the users ’ Web traffic directly and indirectly. In addition, our study provides realistic estimates for certain key parameters used for Web modelling. 1.