Matches in ScholarlyData for { ?s ?p ?o. }
- item-1 hasContent jian-hu.
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- item-2 hasContent gang-wang.
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- item-3 hasContent fred-lochovsky.
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- item-4 hasContent jian-tao-sun.
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- item-5 hasContent zheng-chen.
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- 49 type InProceedings.
- 49 label "Discovering Users' Specific Geo Intention in Web Search".
- 49 sameAs 49.
- 49 abstract "Discovering users' specific and implicit geographic intention in web search can greatly help satisfy users' information needs. We build a geo intent analysis system that uses minimal supervision to learn a model from large amounts of web-search logs for this discovery. We build a city language model, which is a probabilistic representation of the language surrounding the mention of a city in web queries. We use several features derived from these language models to: (1) identify users' implicit geo intent and pinpoint the city corresponding to this intent, (2) determine whether the geo-intent is localized around the users' current geographic location, (3) predict cities for queries that have a mention of an entity that is located in a specific place. Experimental results demonstrate the effectiveness of using features derived from the city language model. We find that (1) the system has over 90% precision and more than 74% accuracy for the task of detecting users' implicit city level geo intent (2) the system achieves more than 96% accuracy in determining whether implicit geo queries are local geo queries, neighbor region geo queries or none-of these (3) the city language model can effectively retrieve cities in locationspecific queries with high precision (88%) and recall (74%); human evaluation shows that the language model predicts city labels for location-specific queries with high accuracy (84.5%).".
- 49 hasAuthorList authorList.
- 49 isPartOf proceedings.
- 49 keyword "Search".
- 49 title "Discovering Users' Specific Geo Intention in Web Search".
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- item-1 hasContent xing-yi.
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- item-2 hasContent hema-raghavan.
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- item-3 type ListItem.
- item-3 hasContent chris-leggetter.
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- 5 type InProceedings.
- 5 label "Estimating the ImpressionRank of Web Pages".
- 5 sameAs 5.
- 5 abstract "The ImpressionRank of a web page (or, more generally, of a web site) is the number of times users viewed the page while browsing search results. ImpressionRank captures the visibility of pages and sites in search engines and is thus an important measure, which is of interest to web site owners, competitors, market analysts, and end users. All previous approaches to estimating the ImpressionRank of a page rely on privileged access to private data sources, like the search engine's query log. In this paper we present the first external algorithm for estimating the ImpressionRank of a web page. This algorithm relies on access to three public data sources: the search engine, the query suggestion service of the search engine, and the web. In addition, the algorithm is local and uses modest resources. It can therefore be used by almost any party to estimate the ImpressionRank of any page on any search engine. En route to estimating the ImpressionRank of a page, our algorithm solves a novel variant of the keyword extraction problem: it finds the most popular search keywords that drive impressions of a page. Empirical analysis of the algorithm on the Google and Yahoo! search engines indicates that it is accurate and provides interesting insights about sites and search queries.".
- 5 hasAuthorList authorList.
- 5 isPartOf proceedings.
- 5 keyword "Data Mining".
- 5 title "Estimating the ImpressionRank of Web Pages".
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- item-1 hasContent ziv-bar-yossef.
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- item-2 hasContent maxim-gurevich.
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- 50 type InProceedings.
- 50 label "A Search-based Method for Forecasting Ad Impression in Contextual Advertising".
- 50 sameAs 50.
- 50 abstract "Contextual advertising (also called content match) refers to the placement of small textual ads within the content of a generic web page. It has become a significant source of revenue for publishers ranging from individual bloggers to major newspapers. At the same time it is an important way for advertisers to reach their intended audience. This reach depends on the total number of exposures of the ad (impressions) and its click-through-rate (CTR) that can be viewed as the probability of an end-user clicking on the ad when shown. These two orthogonal, critical factors are both difficult to estimate and even individually can still be very informative and useful in planning and budgeting advertising campaigns. In this paper, we address the problem of forecasting the number of impressions for new or changed ads in the system. Producing such forecasts, even within large margins of error, is quite challenging: 1) ad selection in contextual advertising is a complicated process based on tens or even hundreds of page and ad features; 2) the publishers' content and traffic vary over time; and 3) the scale of the problem is daunting: over a course of a week it involves billions of impressions, hundreds of millions of distinct pages, hundreds of millions of ads, and varying bids of other competing advertisers. We tackle these complexities by simulating the presence of a given ad with its associated bid over weeks of historical data. We obtain an impression estimate by counting how many times the ad would have been displayed if it were in the system over that period of time. We estimate this count by an efficient two-level search algorithm over the distinct pages in the data set. Experimental results show that our approach can accurately forecast the expected number of impressions of contextual ads in real time. We also show how this method can be used in tools for bid selection and ad evaluation.".
- 50 hasAuthorList authorList.
- 50 isPartOf proceedings.
- 50 keyword "Search".
- 50 title "A Search-based Method for Forecasting Ad Impression in Contextual Advertising".
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- item-1 type ListItem.
- item-1 hasContent xuerui-wang.
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- item-2 hasContent andrei-broder.
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- item-3 hasContent marcus-fontoura.
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- item-4 type ListItem.
- item-4 hasContent vanja-josifovski.
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- 51 type InProceedings.
- 51 label "Exploiting Web Search Engines to Search Structured Databases".
- 51 sameAs 51.
- 51 abstract "Web search engines often federate many user queries to relevant structured databases. For example, a product related query might be federated to a product database containing their descriptions and specifications. The relevant structured data items are then returned to the user along with web search results. However, each structured database is searched in isolation. Hence, the search often produces empty or incomplete results as the database may not contain the required information to answer the query. In this paper, we propose a novel integrated search architecture. We establish and exploit the relationships between web search results and the items in structured databases to identify the relevant structured data items for a much wider range of queries. Our architecture leverages existing search engine components to implement this functionality at very low overhead. We demonstrate the quality and efficiency of our techniques through an extensive experimental study.".
- 51 hasAuthorList authorList.
- 51 isPartOf proceedings.
- 51 keyword "Search".
- 51 title "Exploiting Web Search Engines to Search Structured Databases".
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- authorList hasItem item-1.