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Found 6 papers, 1 papers with code
Transformer-empowered Multi-modal Item Embedding for Enhanced Image Search in E-Commerce
Since its deployment in March 2023, it has achieved a remarkable 9. 90% increase in terms of clicks per user and a 4. 23% boost in terms of orders per user for the image search feature on the Shopee e-commerce platform.
Clustered Embedding Learning for Recommender Systems
However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions on their own; and 2) it may incur unbearably high memory costs when the number of users and items scales up.
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems
Recommender Systems (RSs) in real-world applications often deal with billions of user interactions daily.
Diversity Regularized Interests Modeling for Recommender Systems
Each interest of the user should have a certain degree of distinction, thus we introduce three strategies as the diversity regularized separator to separate multiple user interest vectors.
Hybrid Interest Modeling for Long-tailed Users
In this work, we propose the Hybrid Interest Modeling (HIM) network to hybrid both personalized interest and semi-personalized interest in learning long-tailed users' preferences in the recommendation.
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction
In this paper, we propose a novel neural network framework ESDF to tackle the above three challenges simultaneously.
