Generating alternative queries, also known as query suggestion, has long been proved useful to help a user explore and express his information need. In many scenarios, such suggestions can be generated from a large scale graph of queries and other accessory information, such as the clickthrough. However, how to generate suggestions while ensuring their semantic consistency with the original query remains a challenging problem. In this work, we propose a novel query suggestion algorithm based on ranking queries with the hitting time on a large scale bipartite graph. Without involvement of twisted heuristics or heavy tuning of parameters, this method clearly captures the semantic consistency between the suggested query and the original query. Empirical experiments on a large scale query log of a commercial search engine and a scientific literature collection show that hitting time is effective to generate semantically consistent query suggestions. The proposed algorithm and its variations can successfully boost long tail queries, accommodating personalized query suggestion, as well as finding related authors in research.

We describe the formulation, construction, and evaluation of predictive models of human information seeking from a large dataset of Web search activities. We first introduce an expressive language for describing searching and browsing behavior, and use this language to characterize several prior studies of search behavior. Then, we focus on the construction of predictive models from the data. We review several analyses, including an exploration of the properties of users, queries, and search sessions that are most predictive of future behavior. We also investigate the influence of temporal delay on user actions, and representational tradeoffs with varying the number of steps of user activity considered. Finally, we discuss applications of the predictive models, and focus on the example of performing principled prefetching of content. 1

Long-term search history contains rich information about a user's search preferences. In this paper, we study statistical language modeling based methods to mine contextual information from longterm search history and to exploit it for more accurate estimates of the query model. The experiments on a web search test collection show that the algorithms are effective in improving retrieval accuracy for both fresh and recurring queries. The best performance is achieved when using the combination of related past searches and clickthrough data as the main source of search context.

Familiar evaluation methodologies for information retrieval (IR) are not well suited to the task of comparing systems in many real settings. These systems and evaluation methods must support contextual, interactive retrieval over changing, heterogeneous data collections, including private and confidential information. We have

Web search engines typically provide search results without considering user interests or context. We propose a personalized search approach that can easily extend a conventional search engine on the client side. Our mapping framework automatically maps a set of known user interests onto a group of categories in the Open Directory Project (ODP) and takes advantage of manually edited data available in ODP for training text classifiers that correspond to, and therefore categorize and personalize search results according to user interests. In two sets of controlled experiments, we compare our personalized categorization system (PCAT) with a list interface system (LIST) that mimics a typical search engine and with a nonpersonalized categorization system (CAT). In both experiments, we analyze system performances on the basis of the type of task and query length. We find that PCAT is preferable to LIST for information gathering types of tasks and for searches with short queries, and PCAT outperforms CAT in both information gathering and finding types of tasks, and for searches associated with free-form queries. From the subjects ’ answers to a questionnaire, we find that PCAT is perceived as a system that can find relevant Web pages quicker and easier

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.

Personalized search has much to do with capturing and exploiting user-related context information to improve search accuracy. Existing retrieval systems can not support personalized search well for ignoring a user's search context. In this paper, we describe our ongoing work on the User-Centered Adaptive Information Retrieval (UCAIR) project, which aims at capturing and exploiting naturally available user context for personalized search.

Mining user web search activity potentially has a broad range of applications including web result pre-fetching, automatic search query reformulation, click spam detection, estimation of document relevance and prediction of user satisfaction. This analysis is difficult because the data recorded by search engines while users interact with them, although abundant, is very noisy. In this work, we explore the utility of mining search behavior of users, represented by observed variables including the time the user spends on the page, and whether the user reformulated his or her query. As a case study, we examine the contribution this data makes to predicting the relevance of a document in the absence of document content models. To this end, we first propose a method for grouping the interactions of a particular user according to the different tasks he or she undertakes. With each task corresponding to a distinct information need, we then propose a Bayesian Network to holistically model these interactions. The aim is to identify distinct patterns of search behaviors. Finally, we join these patterns to a list of custom features and we use gradient boosted decision trees to predict the relevance of a set of query document pairs for which we have relevance assessments. The experimental results confirm the potential of our model, with significant improvements in precision for predicting the relevance of documents based on a model of the user’s search and click behavior, over a baseline model using only click and query features, with no Bayesian Network input.

Current Web search tools do a good job of retrieving documents that satisfy the most common intentions associated with a query, but do not do a very good job of discerning different individuals ’ unique search goals. We explore the variation in what different people consider relevant to the same query by mining three data sources: 1) explicit relevance judgments, 2) clicks on search results (a behavior-based implicit measure of relevance), and 3) the similarity of desktop content to search results (a content-based implicit measure of relevance). We find that people’s explicit judgments for the same queries differ greatly. As a result, there is a large gap between how well search engines could perform if they were to tailor results to the individual, and how well they currently perform by returning results designed to satisfy everyone. We call this gap the potential for personalization. The two implicit indicators we studied provide complementary value for approximating this variation in result relevance among people. We discuss several uses of our findings, including a personalized search system that takes advantage of the implicit measures by ranking personally relevant results more highly and improving click-through rates.

I n t hi s paper w e s t udy t er m- based f eedback f or i nf or mat i on r etrieval in the language modeling approach. With term feedback auserdirectly judges the relevance of individual terms without interaction with feedback documents, taking full control of the query expansion process. We propose a cluster-based method for selecting terms to present to the user for judgment, as well as effective algorithms for constructing refined query language models from user term feedback. Our algorithms are shown to bring significant improvement in retrieval accuracy over a non-feedback baseline, and achieve comparable performance to relevance feedback. They are helpful even when there are no relevant documents in the top.