Reproduce “Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning” on OpenResearch
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About this paper
Yijie Shu, Xiaokun Zhang, Youlin Wu, Bo Xu, Liang Yang, Hongfei Lin
Researchers at Dalian University of Technology introduced TDNR-C², a news recommendation framework designed to mitigate clickbait in titles by employing cross-field contrastive learning between news titles and abstracts. This approach led to superior recommendation accuracy on the MIND-small dataset, outperforming baselines across multiple metrics.
Key insights
- News abstracts serve as a more reliable and authentic source of information compared to potentially misleading news titles for accurately representing article content.
- Independent extraction of knowledge from distinct news fields before aggregation is crucial for preventing noise propagation and preserving field-specific nuances in news representations.
- Cross-field contrastive learning, by treating titles and abstracts from the same article as positive pairs, can effectively debias title representations by aligning them with the more truthful content provided in abstracts.
Summary by alphaXiv.
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