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.

Abstract. This paper describes the RMIT group’s participation in the book retrieval task of the INEX booktrack in 2008. Our results suggest that for book retrieval task, using a page-based index and ranking books based on the number of pages retrieved may be more effective than directly indexing and ranking whole books. 1

The Internet has become an important part of the daily life of children as a source of information and leisure activities. Nonetheless, given that most of the content available on the web is aimed at the general public, children are constantly exposed to inappropriate content, either because the language goes beyond their reading skills, their attention span differs from grown-ups or simple because the content is not targeted at children as is the case of ads and adult content. In this work we employed a large query log sample from a commercial web search engine to identify the struggles and search behavior of children of the age of 6 to young adults of the age of 18. Concretely we hypothesized that the large and complex volume of information to which children are exposed leads to ill-defined searches and to disorientation during the search process. For this purpose, we quantified their search difficulties based on query metrics (e.g. fraction of queries posed in natural language), session metrics (e.g. fraction of abandoned sessions) and click activity (e.g. fraction of ad clicks). We also used the search logs to retrace stages of child development. Concretely we looked for changes in the user interests (e.g. distribution of topics searched), language development (e.g. readability of the content accessed) and cognitive development (e.g. sentiment expressed in the queries) among children and adults. We observed that these metrics clearly demonstrate an increased level of confusion and unsuccessful search sessions among children. We also found a clear relation between the reading level oftheclickedpages andthedemographics characteristics of the users such as age and average educational attainment of the zone in which the user is located.

Web search engines can perform poorly for long queries (i.e., those containing four or more terms), in part because of their high level of query specificity. The automatic assignment of labels to long queries can capture aspects of a user’s search intent that may not be apparent from the terms in the query. This affords search result matching or re-ranking based on queries and labels rather than the query text alone. Query labels can be derived from interaction logs generated from many users ’ search result clicks or from query trails comprising the chain of URLs visited following query submission. However, since long queries are typically rare, they are difficult to label in this way because little or no historic log data exists for them. A subset of these queries may be amenable to labeling by detecting similarities between parts of a long and rare query and the queries which appear in logs. In this article, we present the comparison of four similarity algorithms for the automatic assignment of Open Directory Project category labels to long and rare queries, based solely on matching against similar satisfied query trails extracted from log data. Our findings show that although the similarity-matching algorithms we investigated have tradeoffs in terms of coverage and accuracy, one algorithm that bases similarity on a popular search result ranking function (effectively regarding potentially-similar queries as “documents”) outperforms the others.