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.
Papers published on a yearly basis
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12 Aug 2012TL;DR: This paper proposes a time-sensitive approach for query auto-completion that applies time-series and rank candidates according their forecasted frequencies, and suggests that modeling the temporal trends of queries can significantly improve the ranking of QAC candidates.
Abstract: Query auto-completion (QAC) is a common feature in modern search engines. High quality QAC candidates enhance search experience by saving users time that otherwise would be spent on typing each character or word sequentially.Current QAC methods rank suggestions according to their past popularity. However, query popularity changes over time, and the ranking of candidates must be adjusted accordingly. For instance, while halloween might be the right suggestion after typing ha in October, harry potter might be better any other time. Surprisingly, despite the importance of QAC as a key feature in most online search engines, its temporal dynamics have been under-studied.In this paper, we propose a time-sensitive approach for query auto-completion. Instead of ranking candidates according to their past popularity, we apply time-series and rank candidates according their forecasted frequencies. Our experiments on 846K queries and their daily frequencies sampled over a period of 4.5 years show that predicting the popularity of queries solely based on their past frequency can be misleading, and the forecasts obtained by time-series modeling are substantially more reliable. Our results also suggest that modeling the temporal trends of queries can significantly improve the ranking of QAC candidates.
143 citations
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29 Aug 2009TL;DR: This work proposes and evaluates a machine learning-based approach for ranking comments on the Social Web based on the community's expressed preferences, which can be used to promote high-quality comments and filter out low- quality comments.
Abstract: We study how an online community perceives the relative quality of its own user-contributed content, which has important implications for the successful self-regulation and growth of the Social Web in the presence of increasing spam and a flood of Social Web metadata. We propose and evaluate a machine learning-based approach for ranking comments on the Social Web based on the community's expressed preferences, which can be used to promote high-quality comments and filter out low-quality comments. We study several factors impacting community preference, including the contributor's reputation and community activity level, as well as the complexity and richness of the comment. Through experiments, we find that the proposed approach results in significant improvement in ranking quality versus alternative approaches.
143 citations
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TL;DR: In this paper, a query is forwarded to one or more third party search engines, and the responses from the third-party search engine or engines are parsed in order to extract information regarding the documents matching the query.
Abstract: A computer implemented meta search engine and search method. In accordance with this method, a query is forwarded to one or more third party search engines, and the responses from the third party search engine or engines are parsed in order to extract information regarding the documents matching the query. The full text of the documents matching the query are downloaded, and the query terms in the documents are located. The text surrounding the query terms are extracted, and that text is displayed.
142 citations
26 Jan 2011
TL;DR: In this paper, the authors combine the two algorithms by first learning a ranking function with Random Forests and using it as initialization for GBRT, which yields surprisingly accurate ranking results.
Abstract: In May 2010 Yahoo! Inc. hosted the Learning to Rank Challenge. This paper summarizes the approach by the highly placed team Washington University in St. Louis. We investigate Random Forests (RF) as a low-cost alternative algorithm to Gradient Boosted Regression Trees (GBRT) (the de facto standard of web-search ranking). We demonstrate that it yields surprisingly accurate ranking results -- comparable to or better than GBRT. We combine the two algorithms by first learning a ranking function with RF and using it as initialization for GBRT. We refer to this setting as iGBRT. Following a recent discussion by Li et al. (2007), we show that the results of iGBRT can be improved upon even further when the web-search ranking task is cast as classification instead of regression. We provide an upper bound of the Expected Reciprocal Rank (Chapelle et al., 2009) in terms of classification error and demonstrate that iGBRT outperforms GBRT and RF on the Microsoft Learning to Rank and Yahoo Ranking Competition data sets with surprising consistency.
142 citations
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07 Apr 2014TL;DR: The experiments indicate that the combination of a mixture of local low-rank matrices each of which was trained to minimize a ranking loss outperforms many of the currently used state-of-the-art recommendation systems.
Abstract: Personalized recommendation systems are used in a wide variety of applications such as electronic commerce, social networks, web search, and more. Collaborative filtering approaches to recommendation systems typically assume that the rating matrix (e.g., movie ratings by viewers) is low-rank. In this paper, we examine an alternative approach in which the rating matrix is locally low-rank. Concretely, we assume that the rating matrix is low-rank within certain neighborhoods of the metric space defined by (user, item) pairs. We combine a recent approach for local low-rank approximation based on the Frobenius norm with a general empirical risk minimization for ranking losses. Our experiments indicate that the combination of a mixture of local low-rank matrices each of which was trained to minimize a ranking loss outperforms many of the currently used state-of-the-art recommendation systems. Moreover, our method is easy to parallelize, making it a viable approach for large scale real-world rank-based recommendation systems.
142 citations