Enhancing Recommendation Diversity by Re-ranking with Large Language Models Article Swipe
Recommender Systems (RS) should provide diverse recommendations, not just relevant ones. Diversity helps handle uncertainty and offers users meaningful choices. The literature proposes various methods to improve diversity, most notably by re-ranking and selecting from a larger set of candidate recommendations. Driven by promising insights from the literature on how to incorporate versatile Large Language Models (LLMs) into the RS pipeline, in this paper we show how LLMs can be used for diversity re-ranking. We prompt LLMs to generate a diverse ranking from a candidate ranking using various prompt templates with different re-ranking instructions in a zero-shot fashion. We conduct experiments testing state-of-the-art LLMs from the GPT and Llama families. We compare their re-ranking capabilities with random re-ranking and various traditional re-ranking methods from the literature. We open-source the code of our experiments for reproducibility. Our findings suggest that the trade-offs (in terms of performance and costs, among others) of LLM-based re-rankers are superior to those of random re-rankers but, as yet, inferior to the ones of traditional re-rankers. However, because LLMs exhibit improved performance on many natural language processing and recommendation tasks and lower inference costs, we can expect LLM-based re-ranking to become more competitive soon.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3700604
- https://dl.acm.org/doi/pdf/10.1145/3700604
- OA Status
- bronze
- Cited By
- 22
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403431604
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4403431604Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3700604Digital Object Identifier
- Title
-
Enhancing Recommendation Diversity by Re-ranking with Large Language ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-15Full publication date if available
- Authors
-
Diego Carraro, Derek BridgeList of authors in order
- Landing page
-
https://doi.org/10.1145/3700604Publisher landing page
- PDF URL
-
https://dl.acm.org/doi/pdf/10.1145/3700604Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3700604Direct OA link when available
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Diversity (politics), Ranking (information retrieval), Natural language processing, Computer science, Information retrieval, Data science, Geography, Linguistics, Sociology, Anthropology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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22Total citation count in OpenAlex
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2025: 18, 2024: 4Per-year citation counts (last 5 years)
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Diversity | 11 |
| abstract_inverted_index.LLM-based | 150, 190 |
| abstract_inverted_index.candidate | 39, 84 |
| abstract_inverted_index.different | 91 |
| abstract_inverted_index.diversity | 72 |
| abstract_inverted_index.families. | 109 |
| abstract_inverted_index.inference | 185 |
| abstract_inverted_index.pipeline, | 60 |
| abstract_inverted_index.promising | 43 |
| abstract_inverted_index.selecting | 33 |
| abstract_inverted_index.templates | 89 |
| abstract_inverted_index.versatile | 52 |
| abstract_inverted_index.zero-shot | 96 |
| abstract_inverted_index.diversity, | 27 |
| abstract_inverted_index.literature | 21, 47 |
| abstract_inverted_index.meaningful | 18 |
| abstract_inverted_index.processing | 179 |
| abstract_inverted_index.re-rankers | 151, 158 |
| abstract_inverted_index.re-ranking | 31, 92, 113, 117, 121, 191 |
| abstract_inverted_index.trade-offs | 140 |
| abstract_inverted_index.Recommender | 0 |
| abstract_inverted_index.competitive | 195 |
| abstract_inverted_index.experiments | 100, 132 |
| abstract_inverted_index.incorporate | 51 |
| abstract_inverted_index.literature. | 125 |
| abstract_inverted_index.open-source | 127 |
| abstract_inverted_index.performance | 144, 174 |
| abstract_inverted_index.re-rankers. | 168 |
| abstract_inverted_index.re-ranking. | 73 |
| abstract_inverted_index.traditional | 120, 167 |
| abstract_inverted_index.uncertainty | 14 |
| abstract_inverted_index.capabilities | 114 |
| abstract_inverted_index.instructions | 93 |
| abstract_inverted_index.recommendation | 181 |
| abstract_inverted_index.recommendations, | 6 |
| abstract_inverted_index.recommendations. | 40 |
| abstract_inverted_index.reproducibility. | 134 |
| abstract_inverted_index.state-of-the-art | 102 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.99456644 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |