There have been recent interests in studying the "goal" behind a user's Web query, so that this goal can be used to improve the quality of a search engine's results. Previous studies have mainly focused on using manual query-log investigation to identify Web query goals. In this paper we study whether and how we can automate this goal-identification process. We first present our results from a human subject study that strongly indicate the feasibility of automatic query-goal identification. We then propose two types of features for the goal-identification task: user-click behavior and anchor-link distribution. Our experimental evaluation shows that by combining these features we can correctly identify the goals for 90% of the queries studied.

We review a query log of hundreds of millions of queries that constitute the total query traffic for an entire week of a generalpurpose commercial web search service. Previously, query logs have been studied from a single, cumulative view. In contrast, our analysis shows changes in popularity and uniqueness of topically categorized queries across the hours of the day. We examine query traffic on an hourly basis by matching it against lists of queries that have been topically pre-categorized by human editors. This represents 13 % of the query traffic. We show that query traffic from particular topical categories differs both from the query stream as a whole and from other categories. This analysis provides valuable insight for improving retrieval effectiveness and efficiency. It is also relevant to the development of enhanced query disambiguation, routing, and caching algorithms.

The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typical method of learning to rank. We point out that there are two factors one must consider when applying Ranking SVM, in general a “learning to rank” method, to document retrieval. First, correctly ranking documents on the top of the result list is crucial for an Information Retrieval system. One must conduct training in a way that such ranked results are accurate. Second, the number of relevant documents can vary from query to query. One must avoid training a model biased toward queries with a large number of relevant documents. Previously, when existing methods that include Ranking SVM were applied to document retrieval, none of the two factors was taken into consideration. We show it is possible to make modifications in conventional Ranking SVM, so it can be better used for document retrieval. Specifically, we modify the “Hinge Loss ” function in Ranking SVM to deal with the problems described above. We employ two methods to conduct optimization on the loss function: gradient descent and quadratic programming. Experimental results show that our method, referred to as Ranking SVM for IR, can outperform the conventional Ranking SVM and other existing methods for document retrieval on two datasets.

We present a large scale study of search patterns on Google’s mobile search interface. Our goal is to understand the current state of wireless search by analyzing over 1 Million hits to Google's mobile search sites. Our study also includes the examination of search queries and the general categories under which they fall. We follow users throughout multiple interactions to determine search behavior; we estimate how long they spend inputting a query, viewing the search results, and how often they click on a search result. We also compare and contrast search patterns between 12-key keypad phones (cellphones), phones with QWERTY keyboards (PDAs) and conventional computers. Author Keywords Mobile device, cell phone, wireless, search interface

Abstract. We present an analysis of a large blog search engine query log, exploring a number of angles such as query intent, query topics, and user sessions. Our results show that blog searches have different intents than general web searches, suggesting that the primary targets of blog searchers are tracking references to named entities, and locating blogs by theme. In terms of interest areas, blog searchers are, on average, more engaged in technology, entertainment, and politics than web searchers, with a particular interest in current events. The user behavior observed is similar to that in general web search: short sessions with an interest in the first few results only. 1

The aim of this study was to analyze the 2001 Excite query log to investigate the extent and variation of Web queries containing geographic terms. In particular, an investigation into what people search for when they use geographic terms, the ways in which they describe a geographic location, the terminology used to find geographically related information and the structure of users' queries when looking for geographically related information on the Web. This study also attempted to determine how geographically related queries differ from other queries. Geographically related queries formed nearly one fifth of all queries submitted to Excite, the terms occurring most frequently being place names. Geographic queries were also shown to be longer than average and the association of two or more terms within geographic queries was found to be high.

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Being mobile influences not only the types of information people seek but also the ways they attempt to access it. Mobile contexts present challenges of changing location and social context, restricted time for information access, and the need to share attentional resources among concurrent activities. Understanding mobile information needs and associated interaction challenges is fundamental to improving designs for mobile phones and related devices. We conducted a two-week diary study to better understand mobile information needs and how they are addressed. Our study revealed that depending on the time and resources available, as well as the situational context, people use diverse and, at times, ingenious ways to obtain needed information. We summarize key findings and discuss design implications for mobile technology. Author Keywords Diary study, user requirements, mobile devices

Hundreds of millions of users each day use web search engines to meet their information needs. Advances in web search e#ectiveness are therefore perhaps the most significant public outcomes of IR research. Query expansion is one such method for improving the e#ectiveness of ranked retrieval by adding additional terms to a query. In previous approaches to query expansion, the additional terms are selected from highly ranked documents returned from an initial retrieval run. We propose a new method of obtaining expansion terms, based on selecting terms from past user queries that are associated with documents in the collection. Our

We present Aardvark, a social search engine. With Aardvark, users ask a question, either by instant message, email, web input, text message, or voice. Aardvark then routes the question to the person in the user’s extended social network most likely to be able to answer that question. As compared to a traditional web search engine, where the challenge lies in finding the right document to satisfy a user’s information need, the challenge in a social search engine like Aardvark lies in finding the right person to satisfy a user’s information need. Further, while trust in a traditional search engine is based on authority, in a social search engine like Aardvark, trust is based on intimacy. We describe how these considerations inform the architecture, algorithms, and user interface of Aardvark, and how they are reflected in the behavior of Aardvark users.