RecMind aligns LLM text embeddings with LightGCN collaborative embeddings via contrastive learning and a learned gate, achieving the best reported scores on all 8 ranking metrics across two datasets.
Neural News Recommendation with Attentive Multi-View Learning
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abstract
Personalized news recommendation is very important for online news platforms to help users find interested news and improve user experience. News and user representation learning is critical for news recommendation. Existing news recommendation methods usually learn these representations based on single news information, e.g., title, which may be insufficient. In this paper we propose a neural news recommendation approach which can learn informative representations of users and news by exploiting different kinds of news information. The core of our approach is a news encoder and a user encoder. In the news encoder we propose an attentive multi-view learning model to learn unified news representations from titles, bodies and topic categories by regarding them as different views of news. In addition, we apply both word-level and view-level attention mechanism to news encoder to select important words and views for learning informative news representations. In the user encoder we learn the representations of users based on their browsed news and apply attention mechanism to select informative news for user representation learning. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of news recommendation.
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RecMind: LLM-Enhanced Graph Neural Networks for Personalized Consumer Recommendations
RecMind aligns LLM text embeddings with LightGCN collaborative embeddings via contrastive learning and a learned gate, achieving the best reported scores on all 8 ranking metrics across two datasets.