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- 45 type InProceedings.
- 45 label "Nearest-Neighbor Caching for Content-Match Applications".
- 45 sameAs 45.
- 45 abstract "Motivated by contextual advertising systems and other web applications involving efficiency–accuracy tradeoffs, we study similarity caching. Here, a cache hit is said to occur if the requested item is similar but not necessarily equal to some cached item. We study two objectives that dictate the efficiency–accuracy tradeoff and provide our caching policies for these objectives. By conducting extensive experiments on real data we show similarity caching can significantly improve the efficiency of contextual advertising systems, with minimal impact on accuracy. Inspired by the above, we propose a simple generative model that embodies two fundamental characteristics of page requests arriving to advertising systems, namely, long-range dependences and similarities. We provide theoretical bounds on the gains of similarity caching in this model and demonstrate these gains empirically by fitting the actual data to the model.".
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- 45 keyword "Search".
- 45 title "Nearest-Neighbor Caching for Content-Match Applications".
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- item-1 hasContent sandeep-pandey.
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- item-2 hasContent andrei-broder.
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- item-3 hasContent flavio-chierichetti.
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- item-4 hasContent vanja-josifovski.
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- item-5 hasContent ravi-kumar.
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- item-6 hasContent sergei-vassilvitskii.
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- 46 type InProceedings.
- 46 label "Compressed Web Indexes".
- 46 sameAs 46.
- 46 abstract "Web search engines use indexes to efficiently retrieve pages containing specified query terms, as well as pages linking to specified pages. The problem of compressed indexes that permit such fast retrieval has a long history. We consider the problem: assuming that the terms in (or links to) a page are generated from a probability distribution, how well compactly can we build such indexes that allow fast retrieval? Of particular interest is the case when the probability distribution is Zipfian (or a similar power law), since these are the distributions that arise on the web. We obtain sharp bounds on the space requirement of Boolean indexes for text documents that follow Zipf's law. In the process we develop a general technique that applies to any probability distribution, not necessarily a power law; this is the first analysis of compression in indexes under arbitrary distributions. Our bounds lead to quantitative versions of rules of thumb that are folklore in indexing. Our experiments on several document collections show that the distribution of terms appears to follow a double-Pareto law rather than Zipf's law. Despite widely varying sets of documents, the index sizes observed in the experiments conform well to our theoretical predictions.".
- 46 hasAuthorList authorList.
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- 46 keyword "Search".
- 46 title "Compressed Web Indexes".
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- item-1 hasContent flavio-chierichetti.
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- item-2 hasContent ravi-kumar.
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- item-3 hasContent prabhakar-raghavan.
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- 47 type InProceedings.
- 47 label "Unsupervised Query Categorization using Automatically-Built Concept Graphs".
- 47 sameAs 47.
- 47 abstract "Automatic categorization of user queries is an important component of general purpose (Web) search engines, particularly for triggering rich, query-specific content and sponsored links. We propose an unsupervised learning scheme that reduces dramatically the cost of setting up and maintaining such a categorizer, while retaining good categorization power. The model is stored as a graph of concepts where graph edges represent the cross-reference between the concepts. Concepts and relations are extracted from query logs by an offline Web mining process, which uses a search engine as a powerful summarizer for building a concept graph. Empirical evaluation indicates that the system compares favorably on publicly available data sets (such as KDD Cup 2005) as well as on portions of the current query stream of Yahoo! Search, where it is already changing the experience of millions of Web search users.".
- 47 hasAuthorList authorList.
- 47 isPartOf proceedings.
- 47 keyword "Search".
- 47 title "Unsupervised Query Categorization using Automatically-Built Concept Graphs".
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- item-1 hasContent eustache-diemert.
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- item-2 hasContent gilles-vandelle.
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- 48 type InProceedings.
- 48 label "Understanding User's Query Intent with Wikipedia".
- 48 sameAs 48.
- 48 abstract "Understanding the intent behind a user's query can help search engine to automatically route the query to some corresponding vertical search engines to obtain particularly relevant contents, thus, greatly improving user satisfaction. There are three major challenges to the query intent classification problem: (1) Intent representation; (2) Domain coverage and (3) Semantic interpretation. Current approaches to predict the user's intent mainly utilize machine learning techniques. However, it is difficult and often requires many human efforts to meet all these challenges by the statistical machine learning approaches. In this paper, we propose a general methodology to the problem of query intent classification. With very little human effort, our method can discover large quantities of intent concepts by leveraging Wikipedia, one of the best human knowledge base. The Wikipedia concepts are used as the intent representation space, thus, each intent domain is represented as a set of Wikipedia articles and categories. The intent of any input query is identified through mapping the query into the Wikipedia representation space. Compared with previous approaches, our proposed method can achieve much better coverage to classify queries in an intent domain even through the number of seed intent examples is very small. Moreover, the method is very general and can be easily applied to various intent domains. We demonstrate the effectiveness of this method in three different applications, i.e., travel, job, and person name. In each of the three cases, only a couple of seed intent queries are provided. We perform the quantitative evaluations in comparison with two baseline methods, and the experimental results show that our method significantly outperforms other approaches in each intent domain.".
- 48 hasAuthorList authorList.
- 48 isPartOf proceedings.
- 48 keyword "Search".
- 48 title "Understanding User's Query Intent with Wikipedia".
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