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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01 Aug 2009TL;DR: This paper contends that a single, specific ranking function may not suffice for probabilistic databases, and proposes two parameterized ranking functions, called PRFω and PRFe, that generalize or can approximate many of the previously proposed ranking functions.
Abstract: The dramatic growth in the number of application domains that naturally generate probabilistic, uncertain data has resulted in a need for efficiently supporting complex querying and decision-making over such data. In this paper, we present a unified approach to ranking and top-k query processing in probabilistic databases by viewing it as a multi-criteria optimization problem, and by deriving a set of features that capture the key properties of a probabilistic dataset that dictate the ranked result. We contend that a single, specific ranking function may not suffice for probabilistic databases, and we instead propose two parameterized ranking functions, called PRFω and PRFe, that generalize or can approximate many of the previously proposed ranking functions. We present novel generating functions-based algorithms for efficiently ranking large datasets according to these ranking functions, even if the datasets exhibit complex correlations modeled using probabilistic and/xor trees or Markov networks. We further propose that the parameters of the ranking function be learned from user preferences, and we develop an approach to learn those parameters. Finally, we present a comprehensive experimental study that illustrates the effectiveness of our parameterized ranking functions, especially PRFe, at approximating other ranking functions and the scalability of our proposed algorithms for exact or approximate ranking.
151 citations
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TL;DR: This work explores the variation in what different people consider relevant to the same query by mining three data sources, finding that people's explicit judgments for the same queries differ greatly.
Abstract: Current Web search tools do a good job of retrieving documents that satisfy the most common intentions associated with a query, but do not do a very good job of discerning different individuals' unique search goals. We explore the variation in what different people consider relevant to the same query by mining three data sources: (1) explicit relevance judgments, (2) clicks on search results (a behavior-based implicit measure of relevance), and (3) the similarity of desktop content to search results (a content-based implicit measure of relevance). We find that people's explicit judgments for the same queries differ greatly. As a result, there is a large gap between how well search engines could perform if they were to tailor results to the individual, and how well they currently perform by returning results designed to satisfy everyone. We call this gap the potential for personalization. The two implicit indicators we studied provide complementary value for approximating this variation in result relevance among people. We discuss several uses of our findings, including a personalized search system that takes advantage of the implicit measures by ranking personally relevant results more highly and improving click-through rates.
151 citations
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IBM1
TL;DR: In this paper, a system and method for aggregating rankings from a plurality of ranking sources to generate a maximally consistent ranking by minimizing a distance measure is presented, where the ranking sources may be search engines executing queries on web pages that have been deliberately modified to cause an incorrect estimate of their relevance.
Abstract: A system and method for aggregating rankings from a plurality of ranking sources to generate a maximally consistent ranking by minimizing a distance measure. The ranking sources may be search engines executing queries on web pages that have been deliberately modified to cause an incorrect estimate of their relevance. The invention supports combining partial rankings.
151 citations
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TL;DR: In this paper, the authors present two algorithms to prove termination of programs by synthesizing linear ranking functions, using an invariant generator based on iterative forward propagation with widening and extracting ranking functions from the generated invariants.
Abstract: We present two algorithms to prove termination of programs by synthesizing linear ranking functions. The first uses an invariant generator based on iterative forward propagation with widening and extracts ranking functions from the generated invariants by manipulating polyhedral cones. It is capable of finding subtle ranking functions which are linear combinations of many program variables, but is limited to programs with few variables. The second, more heuristic, algorithm targets the class of structured programs with single-variable ranking functions. Its invariant generator uses a heuristic extrapolation operator to avoid iterative forward propagation over program loops. For the programs we have considered, this approach converges faster and the invariants it discovers are sufficiently strong to imply the existence of ranking functions.
151 citations
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28 Jul 2003TL;DR: Results show that the specific technique tested results in longer queries than a standard query elicitation technique, that this technique is indeed usable, that the technique results in increased user satisfaction with the search, and that query length is positively correlated with user satisfactionwith the search.
Abstract: Query length in best-match information retrieval (IR) systems is well known to be positively related to effectiveness in the IR task, when measured in experimental, non-interactive environments. However, in operational, interactive IR systems, query length is quite typically very short, on the order of two to three words. We report on a study which tested the effectiveness of a particular query elicitation technique in increasing initial searcher query length, and which tested the effectiveness of queries elicited using this technique, and the relationship in general between query length and search effectiveness in interactive IR. Results show that the specific technique results in longer queries than a standard query elicitation technique, that this technique is indeed usable, that the technique results in increased user satisfaction with the search, and that query length is positively correlated with user satisfaction with the search.
150 citations