Items where author is affiliated with Google Inc.
Number of items: 5.
Abdel Hamid, Ossama
and Behzadi, Behshad
and Christoph, Stefan
and Henzinger, Monika Detecting the Origin of Text Segments Efficiently.
In the origin detection problem an algorithm is given a set S of documents, ordered by creation time, and a query document D. It needs to output for every consecutive sequence of k alphanumeric terms in D the earliest document in S in which the sequence appeared (if such a document exists). Algorithms for the origin detection problem can, for example, be used to detect the “origin” of text segments in D and thus to detect novel content in D. They can also ﬁnd the document from which the author of D has copied the most (or show that D is mostly original.) We propose novel algorithms for this problem and evaluate them together with a large number of previously published algorithms. Our results show that (1) detecting the origin of text segments efficiently can be done with very high accuracy even when the space used is less than 1% of the size of the documents in S , (2) the precision degrades smoothly with the amount of available space, (3) various estimation techniques can be used to increase the performance of the algorithms.
and Moore, Andrew W. Fast Dynamic Reranking in Large Graphs.
In this paper we consider the problem of re-ranking search results by incorporating user feedback. We present a graph theoretic measure for discriminating irrelevant results from relevant results using a few labeled examples provided by the user. The key intuition is that nodes relatively closer (in graph topology) to the relevant nodes than the irrelevant nodes are more likely to be relevant. We present a simple sampling algorithm to evaluate this measure at speciﬁc nodes of interest, and an efficient branch and bound algorithm to compute the top k nodes from the entire graph under this measure. On quantiﬁable prediction tasks the introduced measure outperforms other diffusion-based proximity measures which take only the positive relevance feedback into account. On the Entity-Relation graph built from the authors and papers of the entire DBLP citation corpus (1.4 million nodes and 2.2 million edges) our branch and bound algorithm takes about 1.5 seconds to retrieve the top 10 nodes w.r.t. this measure with 10 labeled nodes.
and Kossinets, Gueorgi
and Kleinberg, Jon
and Lee, Lillian How Opinions are Received by Online Communities: A Case Study on Amazon.com Helpfulness Votes.
There are many on-line settings in which users publicly express opinions. A number of these offer mechanisms for other users to evaluate these opinions; a canonical example is Amazon.com, where reviews come with annotations like “26 of 32 people found the following review helpful.” Opinion evaluation appears in many off-line settings as well, including market research and political campaigns. Reasoning about the evaluation of an opinion is fundamentally different from reasoning about the opinion itself: rather than asking, “What did Y think of X?”, we are asking, “What did Z think of Y’s opinion of X?” Here we develop a framework for analyzing and modeling opinion evaluation, using a large-scale collection of Amazon book reviews as a dataset. We ﬁnd that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product. As part of our approach, we develop novel methods that take advantage of the phenomenon of review “plagiarism” to control for the effects of text in opinion evaluation, and we provide a simple and natural mathematical model consistent with our ﬁndings. Our analysis also allows us to distinguish among the predictions of competing theories from sociology and social psychology, and to discover unexpected differences in the collective opinion-evaluation behavior of user populations from different countries. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications – Data Mining General Terms: Measurement, Theory Keywords: Review helpfulness, review utility, social inﬂuence, online communities, sentiment analysis, opinion mining, plagiarism.
and Mao, Robert
and Li, Wei The Recurrence Dynamics of Social Tagging.
How often do tags recur? How hard is predicting tag recurrence? What tags are likely to recur? We try to answer these questions by analysing the RSDC08 dataset, in both individual and collective settings. Our ﬁndings provide useful insights for the development of tag suggestion techniques etc.
and Shivakumar, Narayanan Sitemaps: Above and Beyond the Crawl of Duty.
Comprehensive coverage of the public web is crucial to web search engines. Search engines use crawlers to retrieve pages and then discover new ones by extracting the pages’ outgoing links. However, the set of pages reachable from the publicly linked web is estimated to be signiﬁcantly smaller than the invisible web , the set of documents that have no incoming links and can only be retrieved through web applications and web forms. The Sitemaps protocol is a fast-growing web protocol supported jointly by major search engines to help content creators and search engines unlock this hidden data by making it available to search engines. In this paper, we perform a detailed study of how “classic” discovery crawling compares with Sitemaps, in key measures such as coverage and freshness over key representative websites as well as over billions of URLs seen at Google. We observe that Sitemaps and discovery crawling complement each other very well, and offer different tradeoffs. Categories and Subject Descriptors: H.3.3: Information Search and Retrieval. General Terms: Experimentation, Algorithms. Keywords: search engines, crawling, sitemaps, metrics, quality.
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