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
Papers
More filters
•
01 Jan 2009TL;DR: This work defines a new measure, FutureRank, which is the expected future PageRank score based on citations that will be obtained in the future, and shows that FutureRank is accurate and useful for finding and ranking publications.
Abstract: The dynamic nature of citation networks makes the task of ranking scientific articles hard. Citation networks are continually evolving because articles obtain new citations every day. For ranking scientific articles, we can define the popularity or prestige of a paper based on the number of past citations at the user query time; however, we argue that what is most useful is the expected future references. We define a new measure, FutureRank, which is the expected future PageRank score based on citations that will be obtained in the future. In addition to making use of the citation network, FutureRank uses the authorship network and the publication time of the article in order to predict future citations. Our experiments compare FutureRank with existing approaches, and show that FutureRank is accurate and useful for finding and ranking publications.
145 citations
••
24 Jul 2011TL;DR: This paper proposes a Hybrid Factor Non-Negative Matrix Factorization (HF-NMF) approach for item-level social influence modeling, and devise an efficient projected gradient method to solve the HF- NMF problem.
Abstract: People and information are two core dimensions in a social network. People sharing information (such as blogs, news, albums, etc.) is the basic behavior. In this paper, we focus on predicting item-level social influence to answer the question Who should share What, which can be extended into two information retrieval scenarios: (1) Users ranking: given an item, who should share it so that its diffusion range can be maximized in a social network; (2) Web posts ranking: given a user, what should she share to maximize her influence among her friends. We formulate the social influence prediction problem as the estimation of a user-post matrix, in which each entry represents the strength of influence of a user given a web post. We propose a Hybrid Factor Non-Negative Matrix Factorization (HF-NMF) approach for item-level social influence modeling, and devise an efficient projected gradient method to solve the HF-NMF problem. Intensive experiments are conducted and demonstrate the advantages and characteristics of the proposed method.
145 citations
••
TL;DR: The multi‐attribute contractors ranking method bay applying Ordering of feasible alternatives of solutions in terms of preferability technique is presented and an illustrative example of contractor selection is used to demonstrate the feasibility and practicability of the proposed model.
Abstract: Abstract Contractor evaluation is a vital part of the project management cycle and deals with risk and risk management. One of the most important phases in the construction industry is the bidding process. In order to select the most appropriate contractor for the project and prepare the most realistic and accurate bid proposal, stakeholders have to know all financial, technical and general information about these contractors. The information can be determined as qualitative, quantitative or verbal data. This paper presents the multi‐attribute contractors ranking method bay applying Ordering of feasible alternatives of solutions in terms of preferability technique. This method allows dealing with qualitative and quantitative data as well as with data expressed in words (verbal data). Finally, an illustrative example of contractor selection is used to demonstrate the feasibility and practicability of the proposed model.
145 citations
•
05 Aug 2013TL;DR: In this paper, a search system identifies a modifying concept such as a superlative in a received search query, and determines ranking properties based on the modifying concept, and generates a presentation based on a search query.
Abstract: Methods and systems are provided for ranking search results and generating a presentation. In some implementations, a search system generates a presentation based on a search query. In some implementations, a search system ranks search results based on data stored in a knowledge graph. In some implementations, a search system identifies a modifying concept such as a superlative in a received search query, and determines ranking properties based on the modifying concept.
145 citations
••
01 Jun 2000
TL;DR: It was discovered that the ability to maintain search context explicitly seems to affect the way people search, and an efficient implementation of this idea deployed on four search engines: AltaVista, Excite, Google and Hotbot is described.
Abstract: Experienced users who query search engines have a complex behavior. They explore many topics in parallel, experiment with query variations, consult multiple search engines, and gather information over many sessions. In the process they need to keep track of search context — namely useful queries and promising result links, which can be hard. We present an extension to search engines called SearchPad that makes it possible to keep track of ‘search context’ explicitly. We describe an efficient implementation of this idea deployed on four search engines: AltaVista, Excite, Google and Hotbot. Our design of SearchPad has several desirable properties: (i) portability across all major platforms and browsers; (ii) instant start requiring no code download or special actions on the part of the user; (iii) no server side storage; and (iv) no added client–server communication overhead. An added benefit is that it allows search services to collect valuable relevance information about the results shown to the user. In the context of each query SearchPad can log the actions taken by the user, and in particular record the links that were considered relevant by the user in the context of the query. The service was tested in a multi-platform environment with over 150 users for 4 months and found to be usable and helpful. We discovered that the ability to maintain search context explicitly seems to affect the way people search. Repeat SearchPad users looked at more search results than is typical on the Web, suggesting that availability of search context may partially compensate for non-relevant pages in the ranking.
145 citations