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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
14 Jan 2002
TL;DR: In this article, the concepts are organized in primary groups, such as Activities, Objects, Symptoms, and Products groups, which may be implemented as taxonomies, and a list of links or other document indicators tagged to the matched concepts is displayed for the user.
Abstract: This document discusses, among other things, systems, devices, and methods for implementing an efficient and cost-effective automated content provider that effectively steers a user to relevant stored documents. Word or text features are extracted from user query language, and matched to substantially similar concept features. The concepts are organized in primary groups, such as Activities, Objects, Symptoms, and Products groups, which may be implemented as taxonomies. Documents that include the concept feature are tagged to that concept. A list of links or other document indicators tagged to the matched concepts is displayed for the user. Derived groups map relationships between concepts in the same or different primary groups, so that a particular matched concept results in the display of related concepts for restricting or otherwise changing the documents in play that are displayed for the user. This document also describes techniques for ranking the related concepts for display to the user.

123 citations

Posted Content
TL;DR: An in-house implementation of previously reported models are used to do an independent evaluation and an ensemble is created by averaging predictions of multiple models to achieve a state-of-the-art result for the next utterance ranking on the Ubuntu Dialog Corpus.
Abstract: This paper presents results of our experiments for the next utterance ranking on the Ubuntu Dialog Corpus -- the largest publicly available multi-turn dialog corpus. First, we use an in-house implementation of previously reported models to do an independent evaluation using the same data. Second, we evaluate the performances of various LSTMs, Bi-LSTMs and CNNs on the dataset. Third, we create an ensemble by averaging predictions of multiple models. The ensemble further improves the performance and it achieves a state-of-the-art result for the next utterance ranking on this dataset. Finally, we discuss our future plans using this corpus.

123 citations

Journal ArticleDOI
TL;DR: An exploratory evaluation of the metrics shows that even the simplest ones provide statistically significant improvement in the ranking order over the most common algorithmic relevance metric.
Abstract: This paper develops the concept of relevance in the context of learning object search. It proposes a set of metrics to estimate the topical, personal and situational relevance dimensions. These metrics are derived mainly from usage and contextual information. An exploratory evaluation of the metrics shows that even the simplest ones provide statistically significant improvement in the ranking order over the most common algorithmic relevance metric. Moreover, the combination of the metrics through the RankNet learning sorts the result list 50% better than the base-line ranking. The paper also presents open questions in the field of learning object relevance ranking that deserve further attention.

123 citations

Proceedings ArticleDOI
12 Jun 2011
TL;DR: This work proposes DIVGEN, an efficient algorithm for diversity-aware search, which achieves significant performance improvements via novel data access primitives, and devise the first low-overhead data access prioritization scheme with theoretical quality guarantees, and good performance in practice.
Abstract: Typical approaches of ranking information in response to a user's query that return the most relevant results ignore important factors contributing to user satisfaction; for instance, the contents of a result document may be redundant given the results already examined. Motivated by emerging applications, in this work we study the problem of Diversity-Aware Search, the essence of which is ranking search results based on both their relevance, as well as their dissimilarity to other results reported.Diversity-Aware Search is generally a hard problem, and even tractable instances thereof cannot be efficiently solved by adapting existing approaches. We propose DIVGEN, an efficient algorithm for diversity-aware search, which achieves significant performance improvements via novel data access primitives. Although selecting the optimal schedule of data accesses is a hard problem, we devise the first low-overhead data access prioritization scheme with theoretical quality guarantees, and good performance in practice. A comprehensive evaluation on real and synthetic large-scale corpora demonstrates the efficiency and effectiveness of our approach.

123 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