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Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
Deep candidate generation (DCG) that narrows down the collection of rele...
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Contrastive Learning for Debiased Candidate Generation at Scale
Deep candidate generation has become an increasingly popular choice depl...
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Understanding Negative Sampling in Graph Representation Learning
Graph representation learning has been extensively studied in recent yea...
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Controllable Multi-Interest Framework for Recommendation
Recently, neural networks have been widely used in e-commerce recommende...
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ExperienceThinking: Hyperparameter Optimization with Budget Constraints
The problem of hyperparameter optimization exists widely in the real lif...
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Learning Disentangled Representations for Recommendation
User behavior data in recommender systems are driven by the complex inte...
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Dimensional Reweighting Graph Convolutional Networks
Graph Convolution Networks (GCNs) are becoming more and more popular for...
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Cognitive Knowledge Graph Reasoning for One-shot Relational Learning
Inferring new facts from existing knowledge graphs (KG) with explainable...
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Cognitive Graph for Multi-Hop Reading Comprehension at Scale
We propose a new CogQA framework for multi-hop question answering in web...
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Personalized Bundle List Recommendation
Product bundling, offering a combination of items to customers, is one o...
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AliGraph: A Comprehensive Graph Neural Network Platform
An increasing number of machine learning tasks require dealing with larg...
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Deep Interest Evolution Network for Click-Through Rate Prediction
Click-through rate (CTR) prediction, whose goal is to estimate the proba...
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ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation
A user can be represented as what he/she does along the history. A commo...
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