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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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Proceedings ArticleDOI
08 May 2007
TL;DR: An information flow model that leverages diffusion rates for prediction and ranking and a DiffusionRank algorithm is proposed that ranks users based on how quickly information will flow to them.
Abstract: Information flows in a network where individuals influence each other. The diffusion rate captures how efficiently the information can diffuse among the users in the network. We propose an information flow model that leverages diffusion rates for: (1) prediction . identify where information should flow to, and (2) ranking . identify who will most quickly receive the information. For prediction, we measure how likely information will propagate from a specific sender to a specific receiver during a certain time period. Accordingly a rate-based recommendation algorithm is proposed that predicts who will most likely receive the information during a limited time period. For ranking, we estimate the expected time for information diffusion to reach a specific user in a network. Subsequently, a DiffusionRank algorithm is proposed that ranks users based on how quickly information will flow to them. Experiments on two datasets demonstrate the effectiveness of the proposed algorithms to both improve the recommendation performance and rank users by the efficiency of information flow.

138 citations

Journal ArticleDOI
TL;DR: In this paper, the Borda-Kendall method does not perform as claimed in the case of ties and a "minimum variance" method for determining the consensus ranking is proposed and its properties examined.
Abstract: This paper investigates the Borda-Kendall method for the determination of a consensus ranking. It is shown that in the case of ties the method does not perform as claimed. A "minimum variance" method for determining the consensus ranking is proposed and its properties examined. It is shown to be equivalent to the Borda-Kendall method if ties are not allowed. An algorithm to determine the "minimum variance" consensus ranking in the case of ties is described. Results obtained from the solution of problems of various sizes are discussed.

138 citations

Proceedings ArticleDOI
Yujing Hu1, Qing Da1, Anxiang Zeng1, Yang Yu2, Yinghui Xu 
19 Jul 2018
TL;DR: Zhang et al. as discussed by the authors proposed to use reinforcement learning to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session, which can deal with the problem of high reward variance and unbalanced reward distribution of an SSMDP.
Abstract: In E-commerce platforms such as Amazon and TaoBao , ranking items in a search session is a typical multi-step decision-making problem. Learning to rank (LTR) methods have been widely applied to ranking problems. However, such methods often consider different ranking steps in a session to be independent, which conversely may be highly correlated to each other. For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session. Firstly, we formally define the concept of search session Markov decision process (SSMDP) to formulate the multi-step ranking problem. Secondly, we analyze the property of SSMDP and theoretically prove the necessity of maximizing accumulative rewards. Lastly, we propose a novel policy gradient algorithm for learning an optimal ranking policy, which is able to deal with the problem of high reward variance and unbalanced reward distribution of an SSMDP. Experiments are conducted in simulation and TaoBao search engine. The results demonstrate that our algorithm performs much better than the state-of-the-art LTR methods, with more than 40% and 30% growth of total transaction amount in the simulation and the real application, respectively.

138 citations

Proceedings ArticleDOI
26 Apr 2016
TL;DR: This research implemented the weighting of Term Frequency - Inverse Document Frequency (TF-IDF) method and Cosine Similarity with the measuring degree concept of similarity terms in a document to rank the document weight that have closesness match level with expert's document.
Abstract: Development of technology in educational field brings the easier ways through the variety of facilitation for learning process, sharing files, giving assignment and assessment. Automated Essay Scoring (AES) is one of the development systems for determining a score automatically from text document source to facilitate the correction and scoring by utilizing applications that run on the computer. AES process is used to help the lecturers to score efficiently and effectively. Besides it can reduce the subjectivity scoring problem. However, implementation of AES depends on many factors and cases, such as language and mechanism of scoring process especially for essay scoring. A number of methods implemented for weighting the terms from document and reaching the solutions for handling comparative level between documents answer and expert's document still defined. In this research, we implemented the weighting of Term Frequency — Inverse Document Frequency (TF-IDF) method and Cosine Similarity with the measuring degree concept of similarity terms in a document. Tests carried out on a number of Indonesian text-based documents that have gone through the stage of pre-processing for data extraction purposes. This process results is in a ranking of the document weight that have closesness match level with expert's document.

137 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