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- item-1 hasContent karen-church.
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- item-2 hasContent joachim-neumann.
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- 796 creator jiawei-han.
- 796 creator tim-weninger.
- 796 creator william-hsu.
- 796 type InProceedings.
- 796 label "CETR - Content Extraction via Tag Ratios".
- 796 sameAs 796.
- 796 abstract "We present Content Extraction via Tag Ratios (CETR) -- a method to extract content text from diverse Web pages by using the HTML document's tag ratios. We describe how to compute tag ratios on a line-by-line basis and then cluster the resulting histogram into content and non-content areas. Initially, we find that the tag ratio histogram is not easily clustered because of its one-dimensionality; therefore we extend the original approach in order to model the data in two dimensions. Next, we present a tailored clustering technique which operates on the two-dimensional model, and then evaluate our approach against a large set of alternative methods using standard accuracy, precision and recall metrics on a large and varied Web corpus. Finally, we show that, in most cases, CETR achieves better content extraction performance than existing methods, especially across varying web domains, languages and styles.".
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- 796 keyword "Negative content filtering".
- 796 keyword "porn".
- 796 keyword "spam".
- 796 keyword "viruses".
- 796 title "CETR - Content Extraction via Tag Ratios".
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- item-1 hasContent tim-weninger.
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- 801 creator ganesh-agarwal.
- 801 creator govind-kabra.
- 801 creator kevin-chang.
- 801 type InProceedings.
- 801 label "Towards Rich Query Interpretation: Back and Forth on Query Template Mining".
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- 801 abstract "In this paper, we propose to mine templates from search engine query logs, with the goal of rich structured query interpretation. To begin with, we formalize the notion of templates as a sequence of keywords and domain attributes, instantiating many queries based on the instances of these domain attributes. We identify the key challenge in template discovery as the limited seed knowledge. Our solution bootstraps from small seed input to discover relevant query templates, by harnessing the wealth of information available in search logs. We model this information in a tri-partite inference network of queries, sites and templates—together forming the “QueST” network. We propose iterative probabilistic inferencing framework based on dual metrics of precision and recall. We have deployed and tested our algorithm over a real-world large-scale search log of 15 Million queries from the MSN search engine. We find the accuracy of our algorithm to be as much as 90% (on F measure), with very little seed input knowledge and even incomplete domain schema.".
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- 801 keyword "Query log".
- 801 keyword "click trail".
- 801 keyword "traffic data mining".
- 801 title "Towards Rich Query Interpretation: Back and Forth on Query Template Mining".
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- item-1 hasContent govind-kabra.
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- 828 creator nikolay-archak.
- 828 creator s-muthukrishnan.
- 828 creator vahab-mirrokni.
- 828 type InProceedings.
- 828 label "Mining Advertiser-specific User Behavior Using Adfactors".
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- 828 abstract "Consider an online ad campaign run by an advertiser. The ad serving companies that handle such campaigns record users' behavior that leads to impressions of campaign ads, as well as users' responses to such impressions. This is summarized and reported to the advertisers to help them evaluate the performance of their campaigns and make better budget allocation decisions. The most popular reporting statistics are the click-through rate and the conversion rate. While these are indicative of the effectiveness of an ad campaign, advertisers often seek to understand more sophisticated long-term effects of their ads on brand awareness and user behavior that leads to "conversion", thus creating need for reporting measures that can capture duration, frequency and pathways to user conversion. In this paper, we propose an alternative data mining framework for analyzing user-level advertising data. In the aggregation step, we compress individual user histories into a graph structure, called adgraph, representing local correlations between ad events. For the reporting step, we introduce several scoring rules, called adfactors (AF), that can capture global role of ads and ad paths in the adgraph, in particular, the structural correlation between an ad impression and user conversion. We present scalable local algorithms for computing the adfactors; all algorithms were implemented using MapReduce programming model and Pregel framework. Using an anonymous dataset of user-level data for sponsored search campaigns of eight different advertisers, we evaluate our framework with different adgraphs and adfactors in terms of their statistical fit to the data, and show its value for mining and visualizing long-term patterns in the advertising data.".
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- 828 keyword "Machine learning".
- 828 keyword "data mining applied to auction theory".
- 828 keyword "user modeling in the context of Internet monetization".
- 828 title "Mining Advertiser-specific User Behavior Using Adfactors".
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- item-1 hasContent nikolay-archak.