| 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. |
| Approach: | They propose a neural news recommendation approach with multi-head self-attention to model the interactions between words and news and use multi-headed self- attention to capture relatedness between the news. |
| Outcome: | The proposed approach can learn representations from news titles by modeling the interactions between words and users and capture relatedness between the news. |
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| Challenge: | Existing news recommendation methods rely on news click history to model user interest, but data sparsity is a problem . other kinds of user behaviors such as webpage browsing and search queries can provide useful clues of users’ news reading interest. |
| Approach: | They propose to exploit heterogeneous user behaviors to learn news representations from their titles via CNN networks and apply attention networks to select important words. |
| Outcome: | The proposed approach exploits heterogeneous user behaviors on a real-world dataset. |
Neural News Recommendation with Topic-Aware News Representation (P19-1)
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| Challenge: | Existing methods for learning accurate news representations do not consider topic information in news. |
| Approach: | They propose a neural news recommendation approach with topic-aware news representations using CNN networks and attention networks to select important words. |
| Outcome: | The proposed approach is based on a topic-aware news encoder and user encoder. |
Neural News Recommendation with Collaborative News Encoding and Structural User Encoding (2021.findings-emnlp)
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| Challenge: | Existing news recommendation models encode news title and content separately without leveraging the structural correlation of user browsing histories to reflect user interests explicitly. |
| Approach: | They propose a news recommendation framework consisting of collaborative news encoding and structural user encode to enhance news and user representation learning. |
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Neural News Recommendation with Long- and Short-term User Representations (P19-1)
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| Challenge: | Existing news recommendation methods learn a single representation for each user, which may be insufficient. |
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MINER: Multi-Interest Matching Network for News Recommendation (2022.findings-acl)
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| Challenge: | Existing methods learn a single user embedding from user’s historical behaviors to represent the reading interest. |
| Approach: | They propose a poly attention scheme to learn multiple interest vectors for each user, which encodes the different aspects of user interest. |
| Outcome: | The proposed approach significantly outperforms existing state-of-the-art methods on the MIND news recommendation benchmark. |
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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| Outcome: | The proposed approach can improve the sentiment diversity in news recommendation without performance sacrifice. |
Fine-grained Interest Matching for Neural News Recommendation (2020.acl-main)
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| Challenge: | Existing studies represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation. |
| Approach: | They propose a Fine-grained interest matching method for neural news recommendation based on multi-level representations and fine-grain matching between segment pairs of each browsed news and the candidate news at each semantic level. |
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Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation (2022.findings-acl)
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| Challenge: | Existing news recommender systems conduct news recall and ranking separately with different models, but maintaining multiple models leads to high computational cost and high latency. |
| Approach: | They propose a unified method for recall and ranking in news recommendation that uses historical news click behaviors to extract user embeddings for ranking from the user's attention query. |
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DIGAT: Modeling News Recommendation with Dual-Graph Interaction (2022.findings-emnlp)
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| Challenge: | Existing news recommendation methods lack effective news-user feature interaction. |
| Approach: | They propose to use news-graph and user-graph channels to enhance news encodings . they also propose to perform effective feature interaction between news and user graphs based on semantic-augmented graphs. |
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Multi-view Models for Political Ideology Detection of News Articles (D18-1)
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| Challenge: | Existing models for automatic detection of political ideology only leverage textual cues to identify the ideology evinced by a news article. |
| Approach: | They propose a novel attention based multi-view model that leverages cues from textual content and the network structure of news articles to identify political ideology. |
| Outcome: | The proposed model outperforms state of the art models by 10 percentage points on a battery of baselines and compares with baselines. |