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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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Patent
13 May 2005
TL;DR: In this paper, the authors proposed a method for improving user search experience with a search engine by providing a way for associated users to create and share personalized lists of local search results and advertisements through endorsements of such local search result and/or ads.
Abstract: Methods and systems for improving user search experience with a search engine by providing a way for associated users to create and share personalized lists of local search results and/or advertisements through endorsements of such local search results and/or ads. Local search endorsements can be used to personalize the search engine's ranking of local search results by offering a way for users to re-rank the results for themselves and for those who trust them.

194 citations

Proceedings ArticleDOI
19 Jul 2009
TL;DR: This work uses various measures of query quality described in the literature as features to represent sub-queries, and trains a classifier to reduce long queries to more effective shorter ones that lack those extraneous terms.
Abstract: Long queries frequently contain many extraneous terms that hinder retrieval of relevant documents. We present techniques to reduce long queries to more effective shorter ones that lack those extraneous terms. Our work is motivated by the observation that perfectly reducing long TREC description queries can lead to an average improvement of 30% in mean average precision. Our approach involves transforming the reduction problem into a problem of learning to rank all sub-sets of the original query (sub-queries) based on their predicted quality, and selecting the top sub-query. We use various measures of query quality described in the literature as features to represent sub-queries, and train a classifier. Replacing the original long query with the top-ranked sub-query chosen by the ranker results in a statistically significant average improvement of 8% on our test sets. Analysis of the results shows that query reduction is well-suited for moderately-performing long queries, and a small set of query quality predictors are well-suited for the task of ranking sub-queries.

194 citations

Proceedings ArticleDOI
24 Oct 2016
TL;DR: A suite of query expansion methods that are based on word embeddings that use the CBOW embedding approach to select terms that are semantically related to the query and integrate them with the effective pseudo-feedback-based relevance model.
Abstract: We present a suite of query expansion methods that are based on word embeddings. Using Word2Vec's CBOW embedding approach, applied over the entire corpus on which search is performed, we select terms that are semantically related to the query. Our methods either use the terms to expand the original query or integrate them with the effective pseudo-feedback-based relevance model. In the former case, retrieval performance is significantly better than that of using only the query, and in the latter case the performance is significantly better than that of the relevance model.

194 citations

Proceedings ArticleDOI
20 Jul 2008
TL;DR: This paper proposes a K-Nearest Neighbor (KNN) method for query-dependent ranking, and proves a theory which indicates that the approximations are accurate in terms of difference in loss of prediction, if the learning algorithm used is stable with respect to minor changes in training examples.
Abstract: Many ranking models have been proposed in information retrieval, and recently machine learning techniques have also been applied to ranking model construction. Most of the existing methods do not take into consideration the fact that significant differences exist between queries, and only resort to a single function in ranking of documents. In this paper, we argue that it is necessary to employ different ranking models for different queries and onduct what we call query-dependent ranking. As the first such attempt, we propose a K-Nearest Neighbor (KNN) method for query-dependent ranking. We first consider an online method which creates a ranking model for a given query by using the labeled neighbors of the query in the query feature space and then rank the documents with respect to the query using the created model. Next, we give two offline approximations of the method, which create the ranking models in advance to enhance the efficiency of ranking. And we prove a theory which indicates that the approximations are accurate in terms of difference in loss of prediction, if the learning algorithm used is stable with respect to minor changes in training examples. Our experimental results show that the proposed online and offline methods both outperform the baseline method of using a single ranking function.

194 citations

Proceedings ArticleDOI
21 Apr 2008
TL;DR: It is demonstrated that users' post-search browsing activity strongly reflects implicit endorsement of visited pages, which allows estimating topical relevance of Web resources by mining large-scale datasets of search trails.
Abstract: The paper proposes identifying relevant information sources from the history of combined searching and browsing behavior of many Web users. While it has been previously shown that user interactions with search engines can be employed to improve document ranking, browsing behavior that occurs beyond search result pages has been largely overlooked in prior work. The paper demonstrates that users' post-search browsing activity strongly reflects implicit endorsement of visited pages, which allows estimating topical relevance of Web resources by mining large-scale datasets of search trails. We present heuristic and probabilistic algorithms that rely on such datasets for suggesting authoritative websites for search queries. Experimental evaluation shows that exploiting complete post-search browsing trails outperforms alternatives in isolation (e.g., clickthrough logs), and yields accuracy improvements when employed as a feature in learning to rank for Web search.

193 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