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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23 Jul 2007TL;DR: This work compares the performance of this method with other approaches to the problem of incomplete judgements, such as bpref, and shows that the proposed method leads to higher evaluation accuracy, especially if the set of manual judgements is rich in documents, but highly biased against some systems.
Abstract: Information retrieval evaluation based on the pooling method is inherently biased against systems that did not contribute to the pool of judged documents. This may distort the results obtained about the relative quality of the systems evaluated and thus lead to incorrect conclusions about the performance of a particular ranking technique.We examine the magnitude of this effect and explore how it can be countered by automatically building an unbiased set of judgements from the original, biased judgements obtained through pooling. We compare the performance of this method with other approaches to the problem of incomplete judgements, such as bpref, and show that the proposed method leads to higher evaluation accuracy, especially if the set of manual judgements is rich in documents, but highly biased against some systems.
128 citations
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13 Aug 2016TL;DR: This paper introduces three key techniques for base relevance -- ranking functions, semantic matching features and query rewriting, and describes solutions for recency sensitive relevance and location sensitive relevance.
Abstract: Search engines play a crucial role in our daily lives. Relevance is the core problem of a commercial search engine. It has attracted thousands of researchers from both academia and industry and has been studied for decades. Relevance in a modern search engine has gone far beyond text matching, and now involves tremendous challenges. The semantic gap between queries and URLs is the main barrier for improving base relevance. Clicks help provide hints to improve relevance, but unfortunately for most tail queries, the click information is too sparse, noisy, or missing entirely. For comprehensive relevance, the recency and location sensitivity of results is also critical. In this paper, we give an overview of the solutions for relevance in the Yahoo search engine. We introduce three key techniques for base relevance -- ranking functions, semantic matching features and query rewriting. We also describe solutions for recency sensitive relevance and location sensitive relevance. This work builds upon 20 years of existing efforts on Yahoo search, summarizes the most recent advances and provides a series of practical relevance solutions. The performance reported is based on Yahoo's commercial search engine, where tens of billions of urls are indexed and served by the ranking system.
128 citations
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12 Feb 2010TL;DR: In this paper, a semantic search system using a semantic ranking scheme is presented, which includes an ontology analyzer analyzing ontology data related to a search target to determine a weight value of each property according to a weighing method for each property; a semantic path extractor extracting all the semantic paths between resources and query keywords and determining a weighted semantic path according to the semantic path weight value determination scheme.
Abstract: A semantic search system using a semantic ranking scheme including: an ontology analyzer analyzing ontology data related to a search target to determine a weight value of each property according to a weighing method for property; a semantic path extractor extracting all the semantic paths between resources and query keywords and determining a weight value of each extracted semantic path according to the semantic path weight value determination scheme by using the weight value of each property; a relevant resource searcher traversing an instance graph of ontology based on a semantic path having a pre-set length and weight value of more than an expectation level to search resources that have a semantic relationship with the query keywords and are declared as a type presented in the query; and a semantic relevance ranker selecting a top-k results having the highest rank from among the candidate results extracted by the relevant resource researcher by using a relevance scoring function.
127 citations
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08 May 2007TL;DR: A framework for modeling tagging systems and user tagging behavior is introduced and a method for ranking documents matching a tag based on taggers' reliability is described.
Abstract: Tagging systems allow users to interactively annotate a pool of shared resources using descriptive tags. As tagging systems are gaining in popularity, they become more susceptible to tag spam: misleading tags that are generated in order to increase the visibility of some resources or simply to confuse users. We introduce a framework for modeling tagging systems and user tagging behavior. We also describe a method for ranking documents matching a tag based on taggers' reliability. Using our framework, we study the behavior of existing approaches under malicious attacks and the impact of a moderator and our ranking method.
127 citations
01 Jan 2005
TL;DR: In this article, the requirements of advanced text and data-rich applications for an integrated platform are analyzed, and the results of their analyses are cast into a series of challenges to the DB and IR communities.
Abstract: Databases (DB) and information retrieval (IR) have evolved as separate fields. However, modern applications such as customer support, health care, and digital libraries require capabilities for both data and text management. In such settings, traditional DB queries, in SQL or XQuery, are not flexible enough to handle applicationspecific scoring and ranking. IR systems, on the other hand, lack efficient support for handling structured parts of the data and metadata, and do not give the application developer adequate control over the ranking function. This paper analyzes the requirements of advanced text- and data-rich applications for an integrated platform. The core functionality must be manageable, and the API should be easy to program against. A particularly important issue that we highlight is how to reconcile flexibility in scoring and ranking models with optimizability, in order to accommodate a wide variety of target applications efficiently. We discuss whether such a system needs to be designed from scratch, or can be incrementally built on top of existing architectures. The results of our analyses are cast into a series of challenges to the DB and IR communities.
127 citations