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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.13035 |
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| _version_ | 1866916905609068544 |
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| author | Li, Runze Heitz, Lucien Inel, Oana Bernstein, Abraham |
| author_facet | Li, Runze Heitz, Lucien Inel, Oana Bernstein, Abraham |
| contents | This paper introduces Diversity-Driven RandomWalks (D-RDW), a lightweight algorithm and re-ranking technique that generates diverse news recommendations. D-RDW is a societal recommender, which combines the diversification capabilities of the traditional random walk algorithms with customizable target distributions of news article properties. In doing so, our model provides a transparent approach for editors to incorporate norms and values into the recommendation process. D-RDW shows enhanced performance across key diversity metrics that consider the articles' sentiment and political party mentions when compared to state-of-the-art neural models. Furthermore, D-RDW proves to be more computationally efficient than existing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13035 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | D-RDW: Diversity-Driven Random Walks for News Recommender Systems Li, Runze Heitz, Lucien Inel, Oana Bernstein, Abraham Information Retrieval This paper introduces Diversity-Driven RandomWalks (D-RDW), a lightweight algorithm and re-ranking technique that generates diverse news recommendations. D-RDW is a societal recommender, which combines the diversification capabilities of the traditional random walk algorithms with customizable target distributions of news article properties. In doing so, our model provides a transparent approach for editors to incorporate norms and values into the recommendation process. D-RDW shows enhanced performance across key diversity metrics that consider the articles' sentiment and political party mentions when compared to state-of-the-art neural models. Furthermore, D-RDW proves to be more computationally efficient than existing approaches. |
| title | D-RDW: Diversity-Driven Random Walks for News Recommender Systems |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.13035 |