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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.


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Proceedings Article
01 Jan 2007
TL;DR: A new paradigm to enable web search at the object level is explored in this paper, extracting and integrating web information for objects relevant to a specific application domain and ranking these objects in terms of their relevance and popularity in answering user queries.
Abstract: Current web search engines essentially conduct document-level ranking and retrieval. However, structured information about realworld objects embedded in static webpages and online databases exists in huge amounts. We explore a new paradigm to enable web search at the object level in this paper, extracting and integrating web information for objects relevant to a specific application domain. We then rank these objects in terms of their relevance and popularity in answering user queries. In this paper, we introduce the overview and core technologies of object-level vertical search engines that have been implemented in two working systems: Libra Academic Search (http://libra.msra.cn) and Windows Live Product Search (http://products.live.com).

120 citations

Journal ArticleDOI
TL;DR: It is shown that there is no golden recommendation algorithm showing the best performance in all evaluation metrics, and that one should carefully consider the evaluation criteria in choosing a recommendation algorithm for a particular application.
Abstract: Due to the explosion of available information on the Internet, the need for effective means of accessing and processing them has become vital for everyone. Recommender systems have been developed to help users to find what they may be interested in and business owners to sell their products more efficiently. They have found much attention in both academia and industry. A recommender algorithm takes into account user–item interactions, i.e., rating (or purchase) history of users on items, and their contextual information, if available. It then provides a list of potential items for each target user, such that the user is likely to positively rate (or purchase) them. In this paper, we review evaluation metrics used to assess performance of recommendation algorithms. We also survey a number of classical and modern recommendation algorithms and compare their performance in terms of different evaluation metrics on five benchmark datasets. Our experiments show that there is no golden recommendation algorithm showing the best performance in all evaluation metrics. We also find large variability across the datasets. This indicates that one should carefully consider the evaluation criteria in choosing a recommendation algorithm for a particular application.

120 citations

Patent
06 May 2014
TL;DR: In this article, a computer-implemented method and system for enabling communication between networked users based on search queries and common characteristics is disclosed, where the authors relate to receiving a search query from a first user and establishing a communication link between the first users and a second user based on the first user's search query.
Abstract: A computer-implemented method and system for enabling communication between networked users based on search queries and common characteristics is disclosed. Particular embodiments relate to receiving a search query from a first user and establishing a communication link between the first user and a second user based on the first user's search query. Particular embodiments relate to receiving a first search query from a first user, receiving a second search query from a second user, determining if the first user and the second user fit within match criteria, and establishing a communication link between the first user and the second user if the first user and the second user fit within match criteria. Particular embodiments relate to receiving a first search query from a first user, receiving a second search query from a second user, determining if the first search query and the second search query fit within match criteria, determining if the first user and the second user fit within match criteria, and establishing a communication link between the first user and the second user if the first search query and the second search query fit within match criteria and if the first user and the second user fit within match criteria.

120 citations

Journal ArticleDOI
29 Apr 2009-PLOS ONE
TL;DR: A generative model is introduced that explains the simultaneous emergence of bursty nature of rare words and the topical organization of texts and dynamic word ranking and memory across documents as key mechanisms explaining the non trivial organization of written text.
Abstract: Written text is one of the fundamental manifestations of human language, and the study of its universal regularities can give clues about how our brains process information and how we, as a society, organize and share it. Among these regularities, only Zipf's law has been explored in depth. Other basic properties, such as the existence of bursts of rare words in specific documents, have only been studied independently of each other and mainly by descriptive models. As a consequence, there is a lack of understanding of linguistic processes as complex emergent phenomena. Beyond Zipf's law for word frequencies, here we focus on burstiness, Heaps' law describing the sublinear growth of vocabulary size with the length of a document, and the topicality of document collections, which encode correlations within and across documents absent in random null models. We introduce and validate a generative model that explains the simultaneous emergence of all these patterns from simple rules. As a result, we find a connection between the bursty nature of rare words and the topical organization of texts and identify dynamic word ranking and memory across documents as key mechanisms explaining the non trivial organization of written text. Our research can have broad implications and practical applications in computer science, cognitive science and linguistics.

119 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