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AAAI2020推荐相关论文_factorization bandits for interactive recommendati
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· PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media.
· Where to Go Next: Modeling Long-and Short‐Term User Preferences for Point-of‐Interest Recommendation.
· A Knowledge-Aware Attentional Reasoning Network for Recommendation.
· Enhancing Personalized Trip Recommendation with Attractive Routes.
· Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation.
· An Attentional Recurrent Neural Network for Personalized Next Location Recommendation.
· Memory Augmented Graph Neural Networks for Sequential Recommendation.
· Leveraging Title-Abstract Attentive Semantics for Paper Recommendation.
· Diversified Interactive Recommendation with Implicit Feedback.
· Question-driven Purchasing Propensity Analysis for Recommendation.
· Sequential Recommendation with Relation-Aware Kernelized Self-Attention.
· Incremental Fairness in Two‐Sided Market Platforms: On Smoothly Updating Recommendations.
· Attention‐guide Walk Model in Heterogeneous Information Network for Multi-style Recommendation.
· Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data.
· Symmetric Metric Learning with Adaptive Margin for Recommendation.
· Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation.
· Towards Comprehensive Recommender Systems: Time-Aware Unified Recommendations Based on Listwise Ranking of Implicit Cross-Network Data.
· Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback.
· Towards Hands‐free Visual Dialog Interactive Recommendation.
· Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests.
· Stochastically Robust Personalized Ranking for LSH Recommendation Retrieval.
· Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach.
· Multi-Component Graph Convolutional Collaborative Filtering.
· Deep Match to Rank Model for Personalized Click-Through Rate Prediction.
· Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution.
· Improved Algorithms for Conservative Exploration in Bandits.
· Linear Bandits with Feature Feedback.
