MultiLTR: Text Ranking with a Multi-Stage Learning-to-Rank Approach Article Swipe
Hua Yang
,
Teresa Gonçalves
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/info16040308
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/info16040308
The division of retrieval into multiple stages has evolved to balance efficiency and effectiveness among various ranking models. Faster but less accurate models are used to retrieve results from the entire corpus. Slower yet more precise models refine the ranking within the top candidate list. This study proposes a multi-stage learning-to-rank (MultiLTR) method. MultiLTR applies learning-to-rank techniques across multiple stages. It incorporates text from different fields such as titles, body content, and abstracts to produce a more comprehensive and accurate ranking. MultiLTR iteratively refines ranking accuracy through sequential processing phases. It dynamically selects top-performing rankers from a diverse candidate pool at each stage. Experiments were carried out on benchmark datasets, MQ2007 and MQ2008, using three categories of learning-to-rank algorithms. The results demonstrate that MultiLTR outperforms state-of-the-art ranking approaches, particularly in field-based ranking tasks. This study improves ranking accuracy and offers new insights into enhancing multi-stage ranking strategies.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/info16040308
- https://www.mdpi.com/2078-2489/16/4/308/pdf?version=1744621286
- OA Status
- gold
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409437154
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409437154Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/info16040308Digital Object Identifier
- Title
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MultiLTR: Text Ranking with a Multi-Stage Learning-to-Rank ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-13Full publication date if available
- Authors
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Hua Yang, Teresa GonçalvesList of authors in order
- Landing page
-
https://doi.org/10.3390/info16040308Publisher landing page
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https://www.mdpi.com/2078-2489/16/4/308/pdf?version=1744621286Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2078-2489/16/4/308/pdf?version=1744621286Direct OA link when available
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Ranking (information retrieval), Rank (graph theory), Learning to rank, Stage (stratigraphy), Computer science, Information retrieval, Artificial intelligence, Mathematics, Biology, Combinatorics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.titles, | 68 |
| abstract_inverted_index.various | 15 |
| abstract_inverted_index.MultiLTR | 53, 81, 123 |
| abstract_inverted_index.accuracy | 85, 137 |
| abstract_inverted_index.accurate | 21, 79 |
| abstract_inverted_index.content, | 70 |
| abstract_inverted_index.division | 1 |
| abstract_inverted_index.improves | 135 |
| abstract_inverted_index.insights | 141 |
| abstract_inverted_index.multiple | 5, 58 |
| abstract_inverted_index.proposes | 47 |
| abstract_inverted_index.ranking. | 80 |
| abstract_inverted_index.retrieve | 26 |
| abstract_inverted_index.abstracts | 72 |
| abstract_inverted_index.benchmark | 108 |
| abstract_inverted_index.candidate | 43, 98 |
| abstract_inverted_index.datasets, | 109 |
| abstract_inverted_index.different | 64 |
| abstract_inverted_index.enhancing | 143 |
| abstract_inverted_index.retrieval | 3 |
| abstract_inverted_index.(MultiLTR) | 51 |
| abstract_inverted_index.categories | 115 |
| abstract_inverted_index.efficiency | 11 |
| abstract_inverted_index.processing | 88 |
| abstract_inverted_index.sequential | 87 |
| abstract_inverted_index.techniques | 56 |
| abstract_inverted_index.Experiments | 103 |
| abstract_inverted_index.algorithms. | 118 |
| abstract_inverted_index.approaches, | 127 |
| abstract_inverted_index.demonstrate | 121 |
| abstract_inverted_index.dynamically | 91 |
| abstract_inverted_index.field-based | 130 |
| abstract_inverted_index.iteratively | 82 |
| abstract_inverted_index.multi-stage | 49, 144 |
| abstract_inverted_index.outperforms | 124 |
| abstract_inverted_index.strategies. | 146 |
| abstract_inverted_index.incorporates | 61 |
| abstract_inverted_index.particularly | 128 |
| abstract_inverted_index.comprehensive | 77 |
| abstract_inverted_index.effectiveness | 13 |
| abstract_inverted_index.top-performing | 93 |
| abstract_inverted_index.learning-to-rank | 50, 55, 117 |
| abstract_inverted_index.state-of-the-art | 125 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.04992402 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |