In this paper we summarize the analysis performed on the logs of multilingual image search provided by iCLEF09 and its comparison with the logs released in the iCLEF08 campaign. We have processed more than one million log lines in order to identify and characterize 5, 243 individual search sessions

Abstract — During exploratory search sessions, users often experience difficulty finding and re-finding high-quality results that are pertinent to their search goals. Various studies have found that search sessions are unsuccessful, end prematurely, and/or lead to user frustration. Feild and Allan

and focus. Several different approaches to search session segmentation exist, each considering different features of the queries, but it may be helpful to also consider user’s implicit feedback on the search results clicked in response to the query. We propose a method for segmenting queries into search

such queries. We extract such broad latent aspects from query reformulations found in historical search session logs. We propose a framework under which the problem of extracting such broad latent aspects reduces to that of optimizing a formal objective function under constraints on the total number of aspects

Abstract. Typically, three main query reformulation types in sessions are considered: generalization, specification, and drift. We show that given the full context of user interactions, repeat queries represent an important reformulation type which should also be addressed in session retrieval

Previous studies have highlighted the high arrival rate of new content on the web. We study the extent to which this new content can be efficiently discovered by a crawler. Our study has two parts. First, we study the inherent difficulty of the discovery problem using a maximum cover formulation, under an assumption of perfect estimates of likely sources of links to new content. Second, we relax this assumption and study a more realistic setting in which algorithms must use historical statistics to estimate which pages are most likely to yield links to new content. We recommend a simple algorithm that performs comparably to all approaches we consider. We measure the overhead of discovering new content, defined as the average number of fetches required to discover one new page. We show first that with perfect foreknowledge of where to explore for links to new content, it is possible to discover 90 % of all new content with under 3 % overhead, and 100 % of new content with 9 % overhead. But actual algorithms, which do not have access to perfect foreknowledge, face a more difficult task: one quarter of new content is simply not amenable to efficient discovery. Of the remaining three quarters, 80 % of new content during a given week may be discovered with 160 % overhead if content is recrawled fully on a monthly basis.

This paper proposes a random Web crawl model. A Web crawl is a (biased and partial) image of the Web. This paper deals with the hyperlink structure, i.e. a Web crawl is a graph, whose vertices are the pages and whose edges are the hypertextual links. Of course a Web crawl has a very special structure; we recall some known results about it. We then propose a model generating similar structures. Our model simply simulates a crawling, i.e. builds and crawls the graph at the same time. The graphs generated have lot of known properties of Web crawls. Our model is simpler than most random Web graph models, but captures the sames properties. Notice that it models the crawling process instead of the page writing process of Web graph models.

There are many existing studies of user behavior in simple tasks (e.g., navigational and informational search) within a short duration of 1–2 queries. However, we know relatively little about user behavior, especially browsing and clicking behavior, for longer search session solving complex search

In cooperative peer-to-peer information retrieval systems, each node can be considered an intelligent agent and these agents work collectively to provide an information retrieval service. In order to effectively support multiple and concurrent search sessions in the network, we propose two traffic

ABSTRACT This paper performs an analysis on the influence of different factors on users' choices of specific word changes in query reformulation during a search session. We study three types of word changes: whether to remove or retain a word in the current query; whether or not to add a