Papers with personalized news recommendation
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. |
| Outcome: | The proposed method improves recall and ranking efficiency and effectiveness on a benchmark dataset. |
Graph Neural News Recommendation with Unsupervised Preference Disentanglement (2020.acl-main)
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| Challenge: | Existing methods to learn informative user and news representations fail to consider high-order connectivity underlying the user-news interactions. |
| Approach: | They propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement which can encode high-order relationships into user and news representations by information propagation along the graph. |
| Outcome: | The proposed model can encode high-order relationships into user and news representations by information propagation along the graph and disentangle latent preference factors by a neighborhood routing algorithm. |
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation (2021.acl-long)
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| Challenge: | Existing news recommendation methods learn a single user embedding for each user from their previous behaviors to represent their overall interest. Existing methods only learn 'one' embeddable representation vectors to model user interest. |
| Approach: | They propose a news recommendation method with hierarchical user interest modeling that captures user interest in news rather than a single user embedding. |
| Outcome: | The proposed method can better capture multi-grained user interest in news. |
PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity (2021.acl-long)
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| Challenge: | Existing personalized news recommendation methods have difficulties in making accurate recommendations to cold-start users. |
| Approach: | They propose to incorporate news popularity information to improve cold-start recommendations . they propose to use a popularity-aware user encoder to eliminate popularity bias . |
| Outcome: | The proposed method improves accuracy and diversity of personalized news recommendation on two real-world datasets. |