Results 11 to 20 of about 372,511 (339)
A Deep Look into neural ranking models for information retrieval [PDF]
Information Processing & Management, 2019 Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods.
Jiafeng Guo +8 more
semanticscholar +5 more sources
Visual Re-Ranking for Multi-Aspect Information Retrieval [PDF]
Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, 2017 We present visual re-ranking, an interactive visualization technique for multi-aspect information retrieval. In multi-aspect search, the information need of the user consists of more than one aspect or query simultaneously. While visualization and interactive search user interface techniques for improving user interpretation of search results have been
Khalil Klouche +4 more
semanticscholar +6 more sources
Generalized ensemble model for document ranking in information retrieval [PDF]
Computer Science and Information Systems, 2016 A generalized ensemble model (gEnM) for document ranking is proposed in this paper. The gEnM linearly combines the document retrieval models and tries to retrieve relevant documents at high positions. In order to obtain the optimal linear combination of multiple document retrieval models or rankers, an optimization program is formulated by ...
Yanshan Wang, In-Chan Choi, Hongfang Liu
semanticscholar +6 more sources
A multi-dimensional semantic pseudo-relevance feedback framework for information retrieval [PDF]
Scientific ReportsPre-trained models have garnered significant attention in the field of information retrieval, particularly for improving document ranking. Typically, an initial retrieval step using sparse methods such as BM25 is employed to obtain a set of pseudo ...
Min Pan +4 more
doaj +2 more sources
Quantum Approach for Contextual Search, Retrieval, and Ranking of Classical Information
EntropyQuantum-inspired algorithms represent an important direction in modern software information technologies that use heuristic methods and approaches of quantum science. This work presents a quantum approach for document search, retrieval, and ranking based
Alexander P. Alodjants +4 more
doaj +2 more sources
Configurable indexing and ranking for XML information retrieval [PDF]
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, 2004 Indexing and ranking are two key factors for efficient and effective XML information retrieval. Inappropriate indexing may result in false negatives and false positives, and improper ranking may lead to low precisions. In this paper, we propose a configurable XML information retrieval system, in which users can configure appropriate index types for XML
Shaorong Liu, Qinghua Zou, Wesley W. Chu
openalex +3 more sources
On the limitations of document ranking algorithms in information retrieval [PDF]
Proceedings of the 4th annual international ACM SIGIR conference on Information storage and retrieval theoretical issues in information retrieval - SIGIR '81, 1981 A document retrieval system should rank documents in order of their usefulness or satisfaction to the users. This principle was first explicated in the classic paper by Maron and Kuhns (1). Additional considerations concerning document ranking have been suggested by other researchers (2,3).
Keith H. Stirling
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Bandit algorithms in information retrieval evaluation and ranking
Journal of Physics: Conference Series, 2019 Abstract Bandit algorithms have been widely used in many application areas including information retrieval evaluation and ranking. This is largely due to their exceptional performance. The aim of this study is to examine the overall published studies in terms of trends that shape the use of bandit algorithms in the evaluation and ranking
Sinyinda Muwanei +3 more
openalex +3 more sources
Distributed ranking methods for geographic information retrieval [PDF]
, 2004 Geographic Information Retrieval is concerned with retrieving documents that are related to some location. This paper addresses the ranking of documents by both textual relevance and spatial relevance. To this end, we introduce distributed ranking, where similar documents are ranked spreaded in the list instead of sequentially.
Kreveld, Marc van +2 more
openaire +3 more sources
Scalable and Effective Generative Information Retrieval [PDF]
The Web Conference, 2023 Recent research has shown that transformer networks can be used as differentiable search indexes by representing each document as a sequence of document ID tokens.
Hansi Zeng +5 more
semanticscholar +1 more source