MINER: Multi-Interest Matching Network for News Recommendation

Personalized news recommendation is an essential technique to help users find interested news. Accurately matching user's interests and candidate news is the key to news recommendation. Most existing methods learn a single user embedding from user's historical behaviors to represent the reading interest. However, user interest is usually diverse and may not be adequately modeled by a single user embedding. In this paper, we propose a poly attention scheme to learn multiple interest vectors for each user, which encodes the different aspects of user interest. We further propose a disagreement regularization to make the learned interests vectors more diverse. Moreover, we design a category-aware attention weighting strategy that incorporates the news category information as explicit interest signals into the attention mechanism. Extensive experiments on the MIND news recommendation benchmark demonstrate that our approach significantly outperforms existing state-of-the-art methods. 1 https://news.google.com/ 2 https://microsoftnews.msn.com/ 8.13 Category Title Finance 5 excellent dividend stocks to buy for the holiday season. Sports Should NFL be able to fine players for criticizing officiating? Sports 5 takeaways from the 49ers' dominant win over the Panthers. Movies Francis Ford Coppola says Marvel movies are 'despicable'. Sports Magic vs. Cavs Preview: Magic basketball is finally back. Fitness This guy altered his diet and training to drop 65 pounds and pack on muscle.