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Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
Personalized recommender systems are playing an increasingly important r...
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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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U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Learning to rank with implicit feedback is one of the most important tas...
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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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Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
Tagging has been recognized as a successful practice to boost relevance ...
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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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Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
Interactive recommender system (IRS) has drawn huge attention because of...
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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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Personalized Re-ranking for Improving Diversity in Live Recommender Systems
Users of industrial recommender systems are normally suggesteda list of ...
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AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
Learning effective feature interactions is crucial for click-through rat...
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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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An Efficient and Truthful Pricing Mechanism for Team Formation in Crowdsourcing Markets
In a crowdsourcing market, a requester is looking to form a team of work...
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Large-scale Interactive Recommendation with Tree-structured Policy Gradient
Reinforcement learning (RL) has recently been introduced to interactive ...
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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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Collaborative Filtering with Graph-based Implicit Feedback
Introducing consumed items as users' implicit feedback in matrix factori...
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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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