Information Retrieval and Social Media Mining

García, María N. Moreno

doi:10.3390/info11120578

Cited by 3 publications

(1 citation statement)

References 8 publications

(8 reference statements)

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“…These studies focus on different aspects of IR, including ranking algorithms, query comprehension, indexing, and identifying research gaps. Overall, these studies show that VSM-based approaches to IR for search engines are still popular, as is the rising usage of NN-based models, knowledge graphs, deep learning, and reinforcement learning models [30] to increase performance. Some research gaps include the methods' scalability to larger and more diverse datasets, as well as their ability to handle more complex and diverse queries.…”

Section: Literature Surveymentioning

confidence: 98%

Efficient information retrieval model: overcoming challenges in search engines-an overview

Sharma,

Panda

2023

IJEECS

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<span>Search engines play a vital role in information retrieval (IR) indexing and processing vast and diverse data, which now encompasses the ever-expanding wealth of multimedia content. However, search engine performance relies on the efficiency and effectiveness of their information retrieval systems (IRS). To enhance search engine performance, there is a need to develop more efficient and accurate IRS that retrieves relevant information quickly and accurately. To address this challenge, various approaches, including inverted indexing, query expansion, and relevance feedback, have been proposed for IR. Although these approaches have shown promising results, but their effectiveness and limitations require a comprehensive examination This research aims to investigate the challenges and opportunities in designing an efficient IRS for search engines and identify key areas for improvement and future research. The study involves a comprehensive literature review on information retrieval impacting academia, industry, healthcare, e-commerce, and other domains. Researchers rely on search engines to access relevant scientific papers, professionals use them to gather market intelligence, and consumers utilize them for product research and decision-making. The findings of this study will contribute to the development of more efficient and effective information retrieval systems, leading to improved search engine performance and user satisfaction.</span>

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Section: Literature Surveymentioning

confidence: 98%