Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916359566262272 |
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| author | Shu, Yijie Zhang, Xiaokun Wu, Youlin Xu, Bo Yang, Liang Lin, Hongfei |
| author_facet | Shu, Yijie Zhang, Xiaokun Wu, Youlin Xu, Bo Yang, Liang Lin, Hongfei |
| contents | News recommendation emerges as a primary means for users to access content of interest from the vast amount of news. The title clickbait extensively exists in news domain and increases the difficulty for news recommendation to offer satisfactory services for users. Fortunately, we find that news abstract, as a critical field of news, aligns cohesively with the news authenticity. To this end, we propose a Title Debiasing News Recommendation with Cross-field Contrastive learning (TDNR-C2) to overcome the title bias by incorporating news abstract. Specifically, a multi-field knowledge extraction module is devised to extract multi-view knowledge about news from various fields. Afterwards, we present a cross-field contrastive learning module to conduct bias removal via contrasting learned knowledge from title and abstract fileds. Experimental results on a real-world dataset demonstrate the superiority of the proposed TDNR-C2 over existing state-of-the-art methods. Further analysis also indicates the significance of news abstract for title debiasing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning Shu, Yijie Zhang, Xiaokun Wu, Youlin Xu, Bo Yang, Liang Lin, Hongfei Information Retrieval News recommendation emerges as a primary means for users to access content of interest from the vast amount of news. The title clickbait extensively exists in news domain and increases the difficulty for news recommendation to offer satisfactory services for users. Fortunately, we find that news abstract, as a critical field of news, aligns cohesively with the news authenticity. To this end, we propose a Title Debiasing News Recommendation with Cross-field Contrastive learning (TDNR-C2) to overcome the title bias by incorporating news abstract. Specifically, a multi-field knowledge extraction module is devised to extract multi-view knowledge about news from various fields. Afterwards, we present a cross-field contrastive learning module to conduct bias removal via contrasting learned knowledge from title and abstract fileds. Experimental results on a real-world dataset demonstrate the superiority of the proposed TDNR-C2 over existing state-of-the-art methods. Further analysis also indicates the significance of news abstract for title debiasing. |
| title | Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2408.08538 |