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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03 Jul 2014TL;DR: By modeling comments as a time-aware bipartite graph, this work proposes a regularization-based ranking algorithm that accounts for temporal, social influence and current popularity factors to predict the future popularity of items.
Abstract: In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for Web 2.0 items. Incorporating future popularity into ranking is one way to counter this. However, predicting popularity as a third party (as in the case of general search engines) is difficult in practice, due to their limited access to item view histories. To enable popularity prediction externally without excessive crawling, we propose an alternative solution by leveraging user comments, which are more accessible than view counts. Due to the sparsity of comments, traditional solutions that are solely based on view histories do not perform well. To deal with this sparsity, we mine comments to recover additional signal, such as social influence. By modeling comments as a time-aware bipartite graph, we propose a regularization-based ranking algorithm that accounts for temporal, social influence and current popularity factors to predict the future popularity of items. Experimental results on three real-world datasets --- crawled from YouTube, Flickr and Last.fm --- show that our method consistently outperforms competitive baselines in several evaluation tasks.
106 citations
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17 Jul 2006TL;DR: In a new approach to large-scale extraction of facts from unstructured text, distributional similarities become an integral part of both the iterative acquisition of high-coverage contextual extraction patterns and the validation and ranking of candidate facts.
Abstract: In a new approach to large-scale extraction of facts from unstructured text, distributional similarities become an integral part of both the iterative acquisition of high-coverage contextual extraction patterns, and the validation and ranking of candidate facts. The evaluation measures the quality and coverage of facts extracted from one hundred million Web documents, starting from ten seed facts and using no additional knowledge, lexicons or complex tools.
106 citations
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TL;DR: A blackboard-based document management system that uses a neural network spreading-activation algorithm which lets users traverse multiple thesauri is discussed, and the system's query formation; the retrieving, ranking and selection of documents; and thesaurus activation are described.
Abstract: A blackboard-based document management system that uses a neural network spreading-activation algorithm which lets users traverse multiple thesauri is discussed. Guided by heuristics, the algorithm activates related terms in the thesauri and converges of the most pertinent concepts. The system provides two control modes: a browsing module and an activation module that determine the sequence of operations. With the browsing module, users have full control over which knowledge sources to browse and what terms to select. The system's query formation; the retrieving, ranking and selection of documents; and thesaurus activation are described. >
106 citations
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09 Feb 2011TL;DR: It is shown that the transitivity of topical relevance is better preserved over retweet links, and that retweeting a user is a significantly stronger indicator of topical interest than following him, as demonstrated by ranking users with two variants of the PageRank algorithm.
Abstract: Twitter, a micro-blogging platform with an estimated 20 million unique monthly visitors and over 100 million registered users, offers an abundance of rich, structured data at a rate exceeding 600 tweets per second. Recent efforts to leverage this social data to rank users by quality and topical relevance have largely focused on the "follow" relationship. Twitter's data offers additional implicit relationships between users, however, such as "retweets" and "mentions". In this paper we investigate the semantics of the follow and retweet relationships. Specifically, we show that the transitivity of topical relevance is better preserved over retweet links, and that retweeting a user is a significantly stronger indicator of topical interest than following him. We demonstrate these properties by ranking users with two variants of the PageRank algorithm; one based on the follows sub-graph and one based on the implicit retweet sub-graph. We perform a user study to assess the topical relevance of the resulting top-ranked users.
106 citations
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22 Nov 2005TL;DR: In this article, a computer-implemented method for ranking files from an Internet search is provided, which comprises assigning a score to each file based on at least one of the following factors: recency, editorial popularity, clickthru popularity, favorites metadata, or collaborative filtering.
Abstract: A computer-implemented method is provided for ranking files from an Internet search. In one embodiment, the method comprises assigning a score to each file based on at least one of the following factors: recency, editorial popularity, clickthru popularity, favorites metadata, or favorites collaborative filtering. The files may be organized based on the assigned scores to provide users with more accurate search results.
106 citations