News Recommendation
66 papers with code • 1 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in News Recommendation
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Libraries
Use these libraries to find News Recommendation models and implementations
Reddit
MIND
Ranking social media news feed
V-MIND
Reddit Ideology Database
Most implemented papers
Wide & Deep Learning for Recommender Systems
Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance.
Fastformer: Additive Attention Can Be All You Need
In this way, Fastformer can achieve effective context modeling with linear complexity.
NRPA: Neural Recommendation with Personalized Attention
In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews.
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.
Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
\emph{Offline} evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature.
DKN: Deep Knowledge-Aware Network for News Recommendation
To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation.
Neural News Recommendation with Multi-Head Self-Attention
microsoft/recommenders
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IJCNLP 2019
The core of our approach is a news encoder and a user encoder.
News Session-Based Recommendations using Deep Neural Networks
gabrielspmoreira/chameleon_recsys
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This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks.
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
hwwang55/MKR
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Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems.
