Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou
| Challenge: | Personalized news recommendation is an important technique for personalized news service. |
| Approach: | They propose to build a large-scale news recommendation dataset from Microsoft News . they demonstrate that news recommendation relies on the quality of news content understanding . |
| Outcome: | The proposed dataset contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. |
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| Challenge: | Using a dataset of Microsoft News, we propose a generic framework to personalize a text generator and establish personalized headlines. |
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SentiRec: Sentiment Diversity-aware Neural News Recommendation (2020.aacl-main)
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| Challenge: | Existing news recommendation methods rank candidate news based on relevance to users’ historical browsed news, but if browsed data is dominated by certain kinds of sentiment, the model may recommend news with the same sentiment. |
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| Challenge: | Current research in automatic summarisation is expensive to create, posing a challenge for any language. |
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| Challenge: | Precisely modeling news and users is critical for news recommendation, and capturing the contexts of words and news is important to learn news and user representations. |
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| Challenge: | Previous work on target-dependent sentiment classification (TSC) has focused on reviews, social media, and other domains where authors tend to express their opinions explicitly. |
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| Challenge: | Existing personalized news recommendation methods have difficulties in making accurate recommendations to cold-start users. |
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| Challenge: | Existing datasets for supervised news summarization contain considerable amount of noise and expensive training data. |
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Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation (2024.findings-emnlp)
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| Challenge: | Recent work leverages the power of pretrained language models to rank news items . pointwise approaches fail to capture comparative information between items that is more effective for ranking tasks. |
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