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AutoDis: Automatic Discretization for Embedding Numerical Features in CTR Prediction
Learning sophisticated feature interactions is crucial for Click-Through...
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A Practical Incremental Method to Train Deep CTR Models
Deep learning models in recommender systems are usually trained in the b...
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GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
Given the convenience of collecting information through online services,...
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Inter-sequence Enhanced Framework for Personalized Sequential Recommendation
Modeling the sequential correlation of users' historical interactions is...
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Multi-Graph Convolution Collaborative Filtering
Personalized recommendation is ubiquitous, playing an important role in ...
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Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Click-Through Rate prediction is an important task in recommender system...
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Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
Recommendation is crucial in both academia and industry, and various tec...
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An Adjustable Heat Conduction based KNN Approach for Session-based Recommendation
The KNN approach, which is widely used in recommender systems because of...
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Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
User response prediction is a crucial component for personalized informa...
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DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
Learning sophisticated feature interactions behind user behaviors is cri...
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Novel Approaches to Accelerating the Convergence Rate of Markov Decision Process for Search Result Diversification
Recently, some studies have utilized the Markov Decision Process for div...
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DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Learning sophisticated feature interactions behind user behaviors is cri...
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