Search Results for author:
Found 6 papers, 2 papers with code
Neural News Recommendation with Heterogeneous User Behavior
In the user representation module, we propose an attentive multi-view learning framework to learn unified representations of users from their heterogeneous behaviors such as search queries, clicked news and browsed webpages.
Neural News Recommendation with Attentive Multi-View Learning
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.
Ranked #6 on
News Recommendation
on MIND
NPA: Neural News Recommendation with Personalized Attention
Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles.
Neural News Recommendation with Long- and Short-term User Representations
In this paper, we propose a neural news recommendation approach which can learn both long- and short-term user representations.
Ranked #7 on
News Recommendation
on MIND
Neural News Recommendation with Topic-Aware News Representation
The core of our approach is a topic-aware news encoder and a user encoder.
Skeptical Deep Learning with Distribution Correction
Recently deep neural networks have been successfully used for various classification tasks, especially for problems with massive perfectly labeled training data.
