No abstract

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 offer an overview of current Web search engine design. After introducing a generic search engine architecture, we examine each engine component in turn. We cover crawling, local Web page storage, indexing, and the use of link analysis for boosting search performance. The most common design and implementation techniques for each of these components are presented. For this presentation we draw from the literature and from our own experimental search engine testbed. Emphasis is on introducing the fundamental concepts and the results of several performance analyses we conducted to compare different designs.

In this article, we study how we can maintain local copies of remote data sources "fresh," when the source data is updated autonomously and independently. In particular, we study the problem of Web crawlers that maintain local copies of remote Web pages for Web search engines. In this context, remote data sources (Websites) do not notify the copies (Web crawlers) of new changes, so we need to periodically poll the sources to maintain the copies up-to-date. Since polling the sources takes significant time and resources, it is very difficult to keep the copies completely up-to-date.This article proposes various refresh policies and studies their effectiveness. We first formalize the notion of "freshness" of copied data by defining two freshness metrics, and we propose a Poisson process as the change model of data sources. Based on this framework, we examine the effectiveness of the proposed refresh policies analytically and experimentally. We show that a Poisson process is a good model to describe the changes of Web pages and we also show that our proposed refresh policies improve the "freshness" of data very significantly. In certain cases, we got orders of magnitude improvement from existing policies.

Many online data sources are updated autonomously and independently. In this article, we make the case for estimating the change frequency of data to improve Web crawlers, Web caches and to help data mining. We first identify various scenarios, where different applications have different requirements on the accuracy of the estimated frequency. Then we develop several "frequency estimators" for the identified scenarios, showing analytically and experimentally how precise they are. In many cases, our proposed estimators predict change frequencies much more accurately and improve the effectiveness of applications. For example, a Web crawler could achieve 35% improvement in "freshness" simply by adopting our proposed estimator.

One hundred users, one hundred needs. As more and more topics are being discussed on the web and our vocabulary remains relatively stable, it is increasingly difficult to let the search engine know what we want. Coping with ambiguous queries has long been an important part in the research of Information Retrieval, but still remains to be a challenging task. Personalized search has recently got significant attention to address this challenge in the web search community, based on the premise that a user's general preference may help the search engine disambiguate the true intention of a query. However, studies have shown that users are reluctant to provide any explicit input on their personal preference. In this paper, we study how a search engine can learn a user's preference automatically based on her past click history and how it can use the user preference to personalize search results. Our experiments show that users' preferences can be learned accurately even from small click-history data and personalized search based on user preference yields significant improvements over the best existing ranking mechanism in the literature.

Recent studies show that a majority of Web page accesses are referred by search engines. In this paper we study the widespread use of Web search engines and its impact on the ecology of the Web. In particular, we study how much impact search engines have on the popularity evolution of Web pages. For example, given that search engines return currently "popular" pages at the top of search results, are we somehow penalizing newly created pages that are not very well known yet? Are popular pages getting even more popular and new pages completely ignored? We first show that this unfortunate trend indeed exists on the Web through an experimental study based on real Web data. We then analytically estimate how much longer it takes for a new page to attract a large number of Web users when search engines return only popular pages at the top of search results. Our result shows that search engines can have an immensely worrisome impact on the discovery of new Web pages.

In this paper we study how to refresh a local copy of an autonomous data source to maintain the copy up-to-date. As the size of the data grows, it becomes more difficult to maintain the copy "fresh," making it crucial to synchronize the copy effectively. We define two freshness metrics, change models of the underlying data, and synchronization policies. We analytically study how effective the various policies are. We also experimentally verify our analysis, based on data collected from 270 web sites for more than 4 months, and we show that our new policy improves the "freshness" very significantly compared to current policies in use.