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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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Journal ArticleDOI
TL;DR: An end-to-end dual-path convolutional network to learn the image and text representations based on an unsupervised assumption that each image/text group can be viewed as a class, which allows the system to directly learn from the data and fully utilize the supervision.
Abstract: Matching images and sentences demands a fine understanding of both modalities In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space In this field, most existing works apply the ranking loss to pull the positive image / text pairs close and push the negative pairs apart from each other However, directly deploying the ranking loss is hard for network learning, since it starts from the two heterogeneous features to build inter-modal relationship To address this problem, we propose the instance loss which explicitly considers the intra-modal data distribution It is based on an unsupervised assumption that each image / text group can be viewed as a class So the network can learn the fine granularity from every image/text group The experiment shows that the instance loss offers better weight initialization for the ranking loss, so that more discriminative embeddings can be learned Besides, existing works usually apply the off-the-shelf features, ie, word2vec and fixed visual feature So in a minor contribution, this paper constructs an end-to-end dual-path convolutional network to learn the image and text representations End-to-end learning allows the system to directly learn from the data and fully utilize the supervision On two generic retrieval datasets (Flickr30k and MSCOCO), experiments demonstrate that our method yields competitive accuracy compared to state-of-the-art methods Moreover, in language based person retrieval, we improve the state of the art by a large margin The code has been made publicly available

231 citations

20 Jun 2003
TL;DR: This work analytically compares three recent approaches to personalizing PageRank and discusses the tradeoffs of each one.
Abstract: PageRank, the popular link-analysis algorithm for ranking web pages, assigns a query and user independent estimate of "importance" to web pages Query and user sensitive extensions of PageRank, which use a basis set of biased PageRank vectors, have been proposed in order to personalize the ranking function in a tractable way We analytically compare three recent approaches to personalizing PageRank and discuss the tradeoffs of each one

231 citations

Proceedings ArticleDOI
23 Jul 2007
TL;DR: An algorithm named FRank is proposed based on a generalized additive model for the sake of minimizing the fedelity loss and learning an effective ranking function and the experimental results show that the proposed algorithm outperforms other learning-based ranking methods on both conventional IR problem and Web search.
Abstract: Ranking problem is becoming important in many fields, especially in information retrieval (IR). Many machine learning techniques have been proposed for ranking problem, such as RankSVM, RankBoost, and RankNet. Among them, RankNet, which is based on a probabilistic ranking framework, is leading to promising results and has been applied to a commercial Web search engine. In this paper we conduct further study on the probabilistic ranking framework and provide a novel loss function named fidelity loss for measuring loss of ranking. The fidelity loss notonly inherits effective properties of the probabilistic ranking framework in RankNet, but possesses new properties that are helpful for ranking. This includes the fidelity loss obtaining zero for each document pair, and having a finite upper bound that is necessary for conducting query-level normalization. We also propose an algorithm named FRank based on a generalized additive model for the sake of minimizing the fedelity loss and learning an effective ranking function. We evaluated the proposed algorithm for two datasets: TREC dataset and real Web search dataset. The experimental results show that the proposed FRank algorithm outperforms other learning-based ranking methods on both conventional IR problem and Web search.

230 citations

Posted Content
TL;DR: In this paper, the authors developed a methodology for measuring personalization in Web search results and applied their methodology to 200 users on Google Web Search and 100 users on Bing, finding that, on average, 11.7% of results showed differences due to personalization on Google, while 15.8 percent of results were personalized on Bing.
Abstract: Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where different users receive different results for the same search query. The increasing level of personalization is leading to concerns about Filter Bubble effects, where certain users are simply unable to access information that the search engines' algorithm decides is irrelevant. Despite these concerns, there has been little quantification of the extent of personalization in Web search today, or the user attributes that cause it. In light of this situation, we make three contributions. First, we develop a methodology for measuring personalization in Web search results. While conceptually simple, there are numerous details that our methodology must handle in order to accurately attribute differences in search results to personalization. Second, we apply our methodology to 200 users on Google Web Search and 100 users on Bing. We find that, on average, 11.7% of results show differences due to personalization on Google, while 15.8% of results are personalized on Bing, but that this varies widely by search query and by result ranking. Third, we investigate the user features used to personalize on Google Web Search and Bing. Surprisingly, we only find measurable personalization as a result of searching with a logged in account and the IP address of the searching user. Our results are a first step towards understanding the extent and effects of personalization on Web search engines today.

229 citations

Patent
Kelly Wical1
21 May 1997
TL;DR: In this article, a knowledge base search and retrieval system, which includes factual knowledge base queries and concept knowledge base query, is disclosed, which stores associations among terminology/categories that have a lexical, semantical or usage association.
Abstract: A knowledge base search and retrieval system, which includes factual knowledge base queries and concept knowledge base queries, is disclosed. A knowledge base stores associations among terminology/categories that have a lexical, semantical or usage association. Document theme vectors identify the content of documents through themes as well as through classification of the documents in categories that reflects what the documents are primarily about. The factual knowledge base queries identify, in response to an input query, documents relevant to the input query through expansion of the query terms as well as through expansion of themes. The concept knowledge base query does not identify specific documents in response to a query, but specifies terminology that identifies the potential existence of documents in a particular area.

229 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