Topic
Ranking (information retrieval)
About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.
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16 May 2015
TL;DR: MUSE (Method USage Examples), an approach for mining and ranking actual code examples that show how to use a specific method, combines static slicing with clone detection, and uses heuristics to select and rank the best examples in terms of reusability, understandability, and popularity.
Abstract: Code examples are small source code fragments whose purpose is to illustrate how a programming language construct, an API, or a specific function/method works. Since code examples are not always available in the software documentation, researchers have proposed techniques to automatically extract them from existing software or to mine them from developer discussions. In this paper we propose muse (Method USage Examples), an approach for mining and ranking actual code examples that show how to use a specific method. muse combines static slicing (to simplify examples) with clone detection (to group similar examples), and uses heuristics to select and rank the best examples in terms of reusability, understandability, and popularity. muse has been empirically evaluated using examples mined from six libraries, by performing three studies involving a total of 140 developers to: (i) evaluate the selection and ranking heuristics, (ii) provide their perception on the usefulness of the selected examples, and (iii) perform specific programming tasks using the muse examples. The results indicate that muse selects and ranks examples close to how humans do, most of the code examples (82%) are perceived as useful, and they actually help when performing programming tasks.
119 citations
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08 Apr 2013TL;DR: A novel index structure is proposed, called inverted linear quadtree (IL-Quadtree), which is carefully designed to exploit both spatial and keyword based pruning techniques to effectively reduce the search space and a partition based method is proposed to deal with BTOPK-SK.
Abstract: With advances in geo-positioning technologies and geo-location services, there are a rapidly growing amount of spatio-textual objects collected in many applications such as location based services and social networks, in which an object is described by its spatial location and a set of keywords (terms). Consequently, the study of spatial keyword search which explores both location and textual description of the objects has attracted great attention from the commercial organizations and research communities. In the paper, we study the problem of top k spatial keyword search (TOPK-SK), which is fundamental in the spatial keyword queries. Given a set of spatio-textual objects, a query location and a set of query keywords, the top k spatial keyword search retrieves the closest k objects each of which contains all keywords in the query. Based on the inverted index and the linear quadtree, we propose a novel index structure, called inverted linear quadtree (IL-Quadtree), which is carefully designed to exploit both spatial and keyword based pruning techniques to effectively reduce the search space. An efficient algorithm is then developed to tackle top k spatial keyword search. In addition, we show that the IL-Quadtree technique can also be applied to improve the performance of other spatial keyword queries such as the direction-aware top k spatial keyword search and the spatio-textual ranking query. Comprehensive experiments on real and synthetic data clearly demonstrate the efficiency of our methods.
119 citations
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TL;DR: This work argues that this is a better metric than some previously proposed intent aware metrics and shows that it has a better correlation with abandonment rate and proposes an algorithm to rerank web search results based on optimizing an objective function corresponding to this metric.
Abstract: We study the problem of web search result diversification in the case where intent based relevance scores are available. A diversified search result will hopefully satisfy the information need of user-L.s who may have different intents. In this context, we first analyze the properties of an intent-based metric, ERR-IA, to measure relevance and diversity altogether. We argue that this is a better metric than some previously proposed intent aware metrics and show that it has a better correlation with abandonment rate. We then propose an algorithm to rerank web search results based on optimizing an objective function corresponding to this metric and evaluate it on shopping related queries.
119 citations
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04 Feb 2010TL;DR: A machine learning approach for predicting sponsored search ad relevance is described and a novel approach using translation models to learn user click propensity from sparse click logs is presented.
Abstract: We describe a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model to learn from past user clicks on advertisements. We present a novel approach using translation models to learn user click propensity from sparse click logs.Our relevance predictions are then applied to multiple sponsored search applications in both offline editorial evaluations and live online user tests. The predicted relevance score is used to improve the quality of the search page in three areas: filtering low quality ads, more accurate ranking for ads, and optimized page placement of ads to reduce prominent placement of low relevance ads. We show significant gains across all three tasks.
119 citations
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14 Jan 2002
TL;DR: In this article, a system, method, and processor readable medium including processor readable code embodied therein are provided that enable a user to refine a search query using a graphical user interface.
Abstract: A system, method, and processor readable medium including processor readable code embodied therein are provided that enable a user to refine a search query using a graphical user interface. A user may be presented with a graphical user interface (GUI). The GUI may enable the user to input parameters into a first search query. The first search query may be run in a database. The system may determine whether any documents stored in the database satisfy the first search query. If the system determines that one or more documents satisfy the first search query, the system may retrieve a search results that includes the one or more documents. The system may then determine what type of information is included in the search result. Based on the type of information determination, the system may identify a search refinement option that may enable the user to limit the search result. The user may select a search refinement option to limit the search. The search refinement option may be applied to only those documents found in the search result. In this manner, the search query that includes the search refinement option does not need to be run against all of the objects in the database again.
118 citations