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Found 11 papers, 5 papers with code
MINER: Multi-Interest Matching Network for News Recommendation
Accurately matching user’s interests and candidate news is the key to news recommendation.
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data
RAR consists of two key sub-modules, which synergistically gather information from a vast pool of look-alike users and recall items, resulting in enriched user representations.
Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
In the video recommendation, watch time is commonly adopted as an indicator of user interest.
ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
The error memory module is designed with fast access capabilities and undergoes continual refreshing with newly observed data samples during the model serving phase to support fast model adaptation.
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
As such, many two-stream interaction models (e. g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction.
Ranked #1 on
Click-Through Rate Prediction
on MovieLens
BARS: Towards Open Benchmarking for Recommender Systems
Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.
ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems
Deep learning-based recommendation has become a widely adopted technique in various online applications.
Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition
An extension to the pairwise neural ranking is also developed.
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.
Debiased Recommendation with User Feature Balancing
To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing.
Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation
It is still an open question to leverage various types of information under the BERT framework.
