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- item-1 hasContent lei-wu.
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- 38 type InProceedings.
- 38 label "Efficient Interactive Fuzzy Keyword Search".
- 38 sameAs 38.
- 38 abstract "Traditional information systems return answers after a user submits a complete query. Users often feel "left in the dark" when they have limited knowledge about the underlying data, and have to use a try-and-see approach for finding information. A recent trend of supporting autocomplete in these systems is a first step towards solving this problem. In this paper, we study a new information-access paradigm, called "interactive, fuzzy search," in which the system searches the underlying data "on the fly" as the user types in query keywords. It extends autocomplete interfaces by (1) allowing keywords to appear in multiple attributes (in an arbitrary order) of the underlying data; and (2) finding relevant records that have keywords matching query keywords approximately. This framework allows users to explore data as they type, even in the presence of minor errors. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop various incrementalsearch algorithms using previously computed and cached results in order to achieve an interactive speed. We have deployed several real prototypes using these techniques. One of them has been deployed to support interactive search on the UC Irvine people directory, which has been used regularly and well received by users due to its friendly interface and high efficiency. answers. This information-access paradigm requires the user to have certain knowledge about the structure and content of the underlying data repository. In the case where the user has limited knowledge about the data, often the user feels "left in the dark" when issuing queries, and has to use a tryand-see approach for finding information, as illustrated by the following example. At a conference venue, an attendee named John met a person from a university. After the conference he wanted to get more information about this person, such as his research projects. All John knows about the person is that he is a professor from that university, and he only remembers the name roughly. In order to search for this person, John goes to the directory page of the university. Figure 1 shows such an interface. John needs to fill in the form by providing information for multiple attributes, such as name, phone, department, and title. Given his limited information about the person, especially since he does not know the exact spelling of the person's name, John needs to try a few possible keywords, go through the returned results, modify the keywords, and reissue a new query. He needs to repeat this step multiple times to find the person, if lucky enough. This search interface is neither efficient nor user friendly.".
- 38 hasAuthorList authorList.
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- 38 keyword "Search".
- 38 title "Efficient Interactive Fuzzy Keyword Search".
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- item-1 hasContent shengyue-ji.
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- item-3 hasContent chen-li.
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- item-4 hasContent jianhua-feng.
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- 39 type InProceedings.
- 39 label "An Axiomatic Approach for Result Diversification".
- 39 sameAs 39.
- 39 abstract "Understanding user intent is key to designing an effective ranking system in a search engine. In the absence of any explicit knowledge of user intent, search engines want to diversify results to improve user satisfaction. In such a setting, the probability ranking principle-based approach of presenting the most relevant results on top can be sub-optimal, and hence the search engine would like to trade-off relevance for diversity in the results. In analogy to prior work on ranking and clustering systems, we use the axiomatic approach to characterize and design diversification systems. We develop a set of natural axioms that a diversification system is expected to satisfy, and show that no diversification function can satisfy all the axioms simultaneously. We illustrate the use of the axiomatic framework by providing three example diversification objectives that satisfy different subsets of the axioms. We also uncover a rich link to the facility dispersion problem that results in algorithms for a number of diversification objectives. Finally, we propose an evaluation methodology to characterize the objectives and the underlying axioms. We conduct a large scale evaluation of our objectives based on two data sets: a data set derived from the Wikipedia disambiguation pages and a product database.".
- 39 hasAuthorList authorList.
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- 39 keyword "Search".
- 39 title "An Axiomatic Approach for Result Diversification".
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- item-1 hasContent sreenivas-gollapudi.
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- item-2 hasContent aneesh-sharma.
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- 4 type InProceedings.
- 4 label "Fast Dynamic Reranking in Large Graphs".
- 4 sameAs 4.
- 4 abstract "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.".
- 4 hasAuthorList authorList.
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- 4 keyword "Data Mining".
- 4 title "Fast Dynamic Reranking in Large Graphs".
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- item-1 hasContent purnamrita-sarkar.
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- item-2 hasContent andrew-w-moore.
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- 40 type InProceedings.
- 40 label "Quicklink Selection for Navigational Query Results".
- 40 sameAs 40.
- 40 abstract "Quicklinks for a website are navigational shortcuts displayed below the website homepage on a search results page, and that let the users directly jump to selected points inside the website. Since the real-estate on a search results page is constrained and valuable, picking the best set of quicklinks to maximize the benefits for a majority of the users becomes an important problem for search engines. Using user browsing trails obtained from browser toolbars, and a simple probabilistic model, we formulate the quicklink selection problem as a combinatorial optimizaton problem. We first demonstrate the hardness of the objective, and then propose an algorithm that is provably within a factor of (1 − 1/e) of the optimal. We also propose a different algorithm that works on trees and that can find the optimal solution; unlike the previous algorithm, this algorithm can incorporate natural constraints on the set of chosen quicklinks. The efficacy of our methods is demonstrated via empirical results on both a manually labeled set of websites and a set for which quicklink click-through rates for several webpages were obtained from a real-world search engine.".
- 40 hasAuthorList authorList.
- 40 isPartOf proceedings.
- 40 keyword "Search".
- 40 title "Quicklink Selection for Navigational Query Results".
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- item-1 hasContent deepayan-chakrabarti.
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- item-2 hasContent ravi-kumar.