A significant amount of search queries originate from some real world information need or tasks [13]. In order to improve the search experience of the end users, it is important to have accurate representations of tasks. As a result, significant amount of research has been devoted to extracting proper representations of tasks in order to enable search systems to help users complete their tasks, as well as providing the end user with better query suggestions [9], for better recommendations [41], for satisfaction prediction [36] and for improved personalization in terms of tasks [24, 38]. Most existing task extraction methodologies focus on representing tasks as flat structures. However, tasks often tend to have multiple subtasks associated with them and a more naturalistic representation of tasks would be in terms of a hierarchy, where each task can be composed of multiple (sub)tasks. To this end, we propose an efficient Bayesian nonparametric model for extracting hierarchies of such tasks & subtasks. We evaluate our method based on real world query log data both through quantitative and crowdsourced experiments and highlight the importance of considering task subtask hierarchies.
There has been a large body of work focused on the problem of segmenting and organizing query logs into semantically coherent structures. Many such methods use the idea of a cutoff between queries, where two consecutive queries are considered as two different sessions or tasks if the time interval between them exceeds a certain threshold @cite_15 @cite_25 @cite_9 . Often a 30-minute timeout is used to segment sessions. However, experimental results of these methods indicate that the timeouts are of limited utility in predicting whether two queries belong to the same task, and unsuitable for identifying session boundaries.
@cite_9 @cite_15 @cite_25
1982889956 1982896842 2099513082
In this paper we present an analysis of an AltaVista Search Engine query log consisting of approximately 1 billion entries for search requests over a period of six weeks. This represents almost 285 million user sessions, each an attempt to fill a single information need. We present an analysis of individual queries, query duplication, and query sessions. We also present results of a correlation analysis of the log entries, studying the interaction of terms within queries. Our data supports the conjecture that web users differ significantly from the user assumed in the standard information retrieval literature. Specifically, we show that web users type in short queries, mostly look at the first 10 results only, and seldom modify the query. This suggests that traditional information retrieval techniques may not work well for answering web search requests. The correlation analysis showed that the most highly correlated items are constituents of phrases. This result indicates it may be useful for search engines to consider search terms as parts of phrases even if the user did not explicitly specify them as such. Abstract This paper presents the results of a study conducted at Georgia Institute of Technology that captured client-side user events of NCSA's XMosaic. Actual user behavior, as determined from client-side log file analysis, supplemented our understanding of user navigation strategies as well as provided real interface usage data. Log file analysis also yielded design and usability suggestions for WWW pages, sites and browsers. The methodology of the study and findings are discussed along with future research directions. Contextual information provides an important basis for identifying and understanding users' information needs. Our previous work in traditional information retrieval systems has shown how using contextual information could improve retrieval performance. With the vast quantity and variety of information available on the Web, and the short query lengths within Web searches, it becomes even more crucial that appropriate contextual information is extracted to facilitate personalized services. However, finding users' contextual information is not straightforward, especially in the Web search environment where less is known about the individual users. In this paper, we will present an approach that has significant potential far studying Web users' search contexts. The approach automatically groups a user's consecutive search activities on the same search topic into one session. It uses Dempster-Shafer theory to combine evidence extracted from two sources, each of which is based on the statistical data from Web search logs. The evaluation we have performed demonstrates that our approach has achieved a significant improvement over previous methods of session identification.