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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.


Papers
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Proceedings ArticleDOI
01 Jul 1997
TL;DR: This paper compares their scheme of arbitrary passage retrieval to several other document retrieval and passage retrieval methods and shows experimentally that, compared to these methods,ranking via fixed-length passages is robust and effective.
Abstract: Ranking based on passages addresses some of the shortcomings of whole-document ranking. It provides convenient units of text to return to the user, avoids the difficulties of comparing documents of different length, and enables identification of short blocks of relevant material amongst otherwise irrelevant text. In this paper we explore the potential of passage retrieval, based on an experimental evaluation of the ability of passages to identify relevant documents. We compare our scheme of arbitrary passage retrieval to several other document retrieval and passage retrieval methods; we show experimentally that, compared to these methods, ranking via fixed-length passages is robust and effective. Our experiments also show that, compared to whole-document ranking, ranking via fixed-length arbitrary passages significantly improves retrieval effectiveness, by 8% for TREC disks 2 and 4 and by 18%-37% for the Federal Register collection.

299 citations

Proceedings ArticleDOI
28 Jul 2003
TL;DR: A user query classification scheme that uses the difference of distribution, mutual information, the usage rate as anchor texts, and the POS information for the classification and could get the best performance when the OKAPI scoring algorithm was used.
Abstract: The heterogeneous Web exacerbates IR problems and short user queries make them worse. The contents of web documents are not enough to find good answer documents. Link information and URL information compensates for the insufficiencies of content information. However, static combination of multiple evidences may lower the retrieval performance. We need different strategies to find target documents according to a query type. We can classify user queries as three categories, the topic relevance task, the homepage finding task, and the service finding task. In this paper, a user query classification scheme is proposed. This scheme uses the difference of distribution, mutual information, the usage rate as anchor texts, and the POS information for the classification. After we classified a user query, we apply different algorithms and information for the better results. For the topic relevance task, we emphasize the content information, on the other hand, for the homepage finding task, we emphasize the Link information and the URL information. We could get the best performance when our proposed classification method with the OKAPI scoring algorithm was used.

295 citations

Proceedings ArticleDOI
28 Jul 2003
TL;DR: This paper investigates the pre-conditions for successful combination of document representations formed from structural markup for the task of known-item search, and presents a mixture-based language model to investigate several hypotheses.
Abstract: This paper investigates the pre-conditions for successful combination of document representations formed from structural markup for the task of known-item search. As this task is very similar to work in meta-search and data fusion, we adapt several hypotheses from those research areas and investigate them in this context. To investigate these hypotheses, we present a mixture-based language model and also examine many of the current meta-search algorithms. We find that compatible output from systems is important for successful combination of document representations. We also demonstrate that combining low performing document representations can improve performance, but not consistently. We find that the techniques best suited for this task are robust to the inclusion of poorly performing document representations. We also explore the role of variance of results across systems and its impact on the performance of fusion, with the surprising result that the correct documents have higher variance across document representations than highly ranking incorrect documents.

294 citations

Proceedings Article
23 Aug 2010
TL;DR: This paper proposes a new ranking strategy which uses not only the content relevance of a tweet, but also the account authority and tweet-specific features such as whether a URL link is included in the tweet.
Abstract: Twitter, as one of the most popular micro-blogging services, provides large quantities of fresh information including real-time news, comments, conversation, pointless babble and advertisements. Twitter presents tweets in chronological order. Recently, Twitter introduced a new ranking strategy that considers popularity of tweets in terms of number of retweets. This ranking method, however, has not taken into account content relevance or the twitter account. Therefore a large amount of pointless tweets inevitably flood the relevant tweets. This paper proposes a new ranking strategy which uses not only the content relevance of a tweet, but also the account authority and tweet-specific features such as whether a URL link is included in the tweet. We employ learning to rank algorithms to determine the best set of features with a series of experiments. It is demonstrated that whether a tweet contains URL or not, length of tweet and account authority are the best conjunction.

293 citations

Patent
03 Nov 1998
TL;DR: In this article, a method and apparatus for retrieving documents from a collection of documents based on information other than the contents of a desired document is provided for retrieval of documents from the Web.
Abstract: A method and apparatus are provided for retrieving documents from a collection of documents based on information other than the contents of a desired document. The collection of documents, which may be a hypertext system or documents available via the World Wide Web, is indexed. In one embodiment, an indexing process of a search engine receives one or more specifications that identify documents, or document locations, and non-content information such as a tag word or code word. The indexing process searches the index to identify all documents in the index that match one or more of the specifications. If a match is found, the tag word is added to the index, and information about the matching document is stored in the index in association with the tag word. A search query is submitted to the search engine. The search query is automatically modified to add a reference to the tag word, such as a query term that will exclude any index entry for a document associated with the tag word. The search is executed against the index, and a set of search results is generated. Accordingly, the search results automatically exclude all documents associated with the tag word. These techniques may be used, for example, to implement a Web search service that produces more accurate search results or that prevents certain documents, such as pornographic materials, from appearing in search results.

292 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168