Results 11 to 20 of about 250,954 (220)

Information Retrieval in Folksonomies: Search and Ranking [PDF]

Extended Semantic Web Conference, 2006
Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited.
Andreas Hotho, Robert Jäschke, Christoph Schmitz, Gerd Stumme   +3 more
openaire   +2 more sources

Quantum Approach for Contextual Search, Retrieval, and Ranking of Classical Information [PDF]

Entropy
Quantum-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, Anna E. Avdyushina, Dmitriy V. Tsarev, Igor A. Bessmertny, Andrey Yu. Khrennikov   +4 more
doaj   +2 more sources

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, Hoo Wai Lam, Sri Devi Ravana, Douglas Kunda   +3 more
openalex   +2 more sources

Information Retrieval Models and Relevancy Ranking

, 2008
In "Information Retrieval", relevance is a numerical score assigned to a search result, representing how well the results meet the information need of the user that issued the search query. In many cases, a result's relevance determines the order in which it is presented to the user.
Mohammad Ali Norozi
openalex   +2 more sources

2P-BEnc: A two-phase information retrieval and ranking system based on the BERT encoder

Ain Shams Engineering Journal
Information retrieval methods have been advanced by the development of Natural Language Understanding (NLU). The development of deep neural networks was a key driver in the creation of effective Language Models (LM: statistical models trained to ...
Sunil Kumar, Divya Rohatgi, Navin Prakash, Shubham Sahai, Saurav Chandra, Suman Kumar Mishra, Arshad Ali, Munish Kumar   +7 more
doaj   +2 more sources

Scalable Content-Based Ranking in P2P Information Retrieval

, 2008
Numerous retrieval models have been defined within the field of information retrieval (IR) to produce a ranked and ordered list of documents relevant to a given query. Existing models are in general well-explored and thoroughly evaluated using traditionally centralized IR engines.
Maroje Puh, Toan Luu, Ivana Podnar Žarko, Martin Rajman   +3 more
openalex   +4 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, Chen Luo, Bowen Jin, Sheikh Muhammad Sarwar, Tianxin Wei, Hamed Zamani   +5 more
semanticscholar   +1 more source

NevIR: Negation in Neural Information Retrieval [PDF]

Conference of the European Chapter of the Association for Computational Linguistics, 2023
Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no ...
Orion Weller, Dawn J Lawrie, Benjamin Van Durme   +2 more
semanticscholar   +1 more source

Cross-Modal Retrieval by Class Information and Listwise Ranking

Jisuanji kexue yu tansuo, 2021
Cross-modal retrieval has attracted significant attention due to the increasing use of multi-modal data. A major challenge for cross-modal retrieval is the modal gap. To cope with the heterogeneity, common subspace learning method is proposed.
LIU Yuping, GE Hong, ZENG Yibin
doaj   +1 more source

Improving Cross-lingual Information Retrieval on Low-Resource Languages via Optimal Transport Distillation [PDF]

Web Search and Data Mining, 2023
Benefiting from transformer-based pre-trained language models, neural ranking models have made significant progress. More recently, the advent of multilingual pre-trained language models provides great support for designing neural cross-lingual retrieval
Zhiqi Huang
semanticscholar   +1 more source