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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.


Papers
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Journal ArticleDOI
TL;DR: The general architecture and function of an intelligent recommendation system aimed at supporting a leisure traveller in the task of selecting a tourist destination, bundling a set of products and composing a plan for the travel is described.
Abstract: This paper describes the general architecture and function of an intelligent recommendation system aimed at supporting a leisure traveller in the task of selecting a tourist destination, bundling a set of products and composing a plan for the travel. The system enables the user to identify his own destination and to personalize the travel by aggregating elementary items (additional locations to visit, services and activities). Case-Based Reasoning techniques enable the user to browse a repository of past travels and make possible the ranking of the elementary items included in a recommendation when these are selected from a catalogue. The system integrates data and information originating from external, already existent, tourist portals exploiting an XML-based mediator architecture, data mapping techniques, similarity-based retrieval and online analytical processing.

136 citations

Proceedings ArticleDOI
09 Feb 2009
TL;DR: An algorithm to "groupize" (versus "personalize") Web search results that leads to a significant improvement in result ranking on group-relevant queries.
Abstract: Personalized Web search takes advantage of information about an individual to identify the most relevant results for that person. A challenge for personalization lies in collecting user profiles that are rich enough to do this successfully. One way an individual's profile can be augmented is by using data from other people. To better understand whether groups of people can be used to benefit personalized search, we explore the similarity of query selection, desktop information, and explicit relevance judgments across people grouped in different ways. The groupings we explore fall along two dimensions: the longevity of the group members' relationship, and how explicitly the group is formed. We find that some groupings provide valuable insight into what members consider relevant to queries related to the group focus, but that it can be difficult to identify valuable groups implicitly. Building on these findings, we explore an algorithm to "groupize" (versus "personalize") Web search results that leads to a significant improvement in result ranking on group-relevant queries.

136 citations

Proceedings ArticleDOI
11 Jul 2021
TL;DR: Zhang et al. as mentioned in this paper investigated different training strategies for dense retrieval models and tried to explain why hard negative sampling performs better than random sampling, and proposed two training strategies named a stable training algorithm for dense retrieval (STAR) and a query-side training Algorithm for Directly Optimizing Ranking pErformance (ADORE), respectively.
Abstract: Ranking has always been one of the top concerns in information retrieval researches. For decades, the lexical matching signal has dominated the ad-hoc retrieval process, but solely using this signal in retrieval may cause the vocabulary mismatch problem. In recent years, with the development of representation learning techniques, many researchers turn to Dense Retrieval (DR) models for better ranking performance. Although several existing DR models have already obtained promising results, their performance improvement heavily relies on the sampling of training examples. Many effective sampling strategies are not efficient enough for practical usage, and for most of them, there still lacks theoretical analysis in how and why performance improvement happens. To shed light on these research questions, we theoretically investigate different training strategies for DR models and try to explain why hard negative sampling performs better than random sampling. Through the analysis, we also find that there are many potential risks in static hard negative sampling, which is employed by many existing training methods. Therefore, we propose two training strategies named a Stable Training Algorithm for dense Retrieval (STAR) and a query-side training Algorithm for Directly Optimizing Ranking pErformance (ADORE), respectively. STAR improves the stability of DR training process by introducing random negatives. ADORE replaces the widely-adopted static hard negative sampling method with a dynamic one to directly optimize the ranking performance. Experimental results on two publicly available retrieval benchmark datasets show that either strategy gains significant improvements over existing competitive baselines and a combination of them leads to the best performance.

136 citations

Posted Content
TL;DR: It is shown that the relatively simple aNMM model can significantly outperform other neural network models that have been used for the question answering task, and is competitive with models that are combined with additional features.
Abstract: As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers. To achieve good results, however, these models have been combined with additional features such as word overlap or BM25 scores. Without this combination, these models perform significantly worse than methods based on linguistic feature engineering. In this paper, we propose an attention based neural matching model for ranking short answer text. We adopt value-shared weighting scheme instead of position-shared weighting scheme for combining different matching signals and incorporate question term importance learning using question attention network. Using the popular benchmark TREC QA data, we show that the relatively simple aNMM model can significantly outperform other neural network models that have been used for the question answering task, and is competitive with models that are combined with additional features. When aNMM is combined with additional features, it outperforms all baselines.

136 citations

Patent
30 Jan 2007
TL;DR: In this article, the authors proposed a method and system for assigning ranking scores to Internet users of a networking site and a content site in which each user becomes associated with other users as friends and in which the content site enables each user to rate content published by other users.
Abstract: A method and system for assigning ranking scores to Internet users of a networking site and a content site in which the networking site enables each user to become associated with other users as friends and in which the content site enables each user to rate content published by other users includes the following operations. Data indicative of the friends of each user is obtained from the networking site and a network popularity rating is assigned to each user based on this data. Data indicative of the ratings assigned to the published content of the users is obtained from the content site and a content popularity rating is assigned to each user based on this data. A ranking score is assigned to each user based on the network popularity and content popularity ratings of the user. The ranking scores are provided to a third party.

136 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