Personalization has been deemed one of the major challenges in information retrieval with a significant potential for providing better search experience to individual users. Especially, the need for enhanced user models better capturing elements such as users' goals, tasks, and contexts has been identified. In this paper, we introduce a statistical language model for user tasks representing different granularity levels of a user profile, ranging from very specific search goals to broad topics. We propose a personalization framework that selectively matches the actual user information need with relevant past user tasks, and allows to dynamically switch the course of personalization from re-finding very precise information to biasing results to general user interests. In the extreme, our model is able to detect when the user's search and browse history is not appropriate for aiding the user in satisfying her current information quest. Instead of blindly applying personalization to all user queries, our approach refrains from undue actions in these cases, accounting for the user's desire of discovering new topics, and changing interests over time. The effectiveness of our method is demonstrated by an empirical user study.

Search personalization has been pursued in many ways, in order to\nprovide better result rankings and better overall search experience\nto individual users.\nHowever, blindly applying personalization to all user queries, for example,\nby a background model derived from the user's long-term query-and-click\nhistory, is not always appropriate for aiding the user in accomplishing her \nactual task.\nUser interests change over time, a user sometimes works on very different \ncategories of tasks\nwithin a short timespan, and history-based personalization\nmay impede a user's desire of discovering new topics.\nIn this paper we propose a personalization framework that is\nselective in a twofold sense. First, it selectively employs\npersonalization techniques for queries that are expected to benefit from prior \nhistory\ninformation, while refraining from undue actions otherwise.\nSecond, we introduce the notion of tasks representing\ndifferent granularity levels of a user profile, ranging from very\nspecific search goals to broad topics, and base our reasoning selectively\non query-relevant user tasks.\nThese considerations are cast into a statistical language model for tasks, \nqueries, and\ndocuments, supporting both judicious query expansion and result re-ranking.\nThe effectiveness of our method is demonstrated by an empirical user study.

Search engines are a vital part of the Web and thus the Internet infrastructure. Therefore understanding the behavior of users searching the Web gives insights into trends, and enables enhancements of future search capabilities. Possible data sources for studying Web search behavior are either server-side logs or client-side logs. Unfortunately, current server-side logs are hard to obtain as they are considered proprietary by the search engine operators. Therefore we in this paper present a methodology for extracting client-side logs from the traffic exchanged between a large user group and the Internet. The added benefit of our methodology is that we do not only extract the search terms, the query sequences, and search results of each individual user but also the full clickstream, i.e., the result pages users view and the subsequently visited hyperlinked pages. We propose a finite-state Markov model that captures the user web searching and browsing behavior and allows us to deduce users' prevalent search patterns. To our knowledge, this is the first such detailed client-side analysis of clickstreams.