AAAI2020推荐相关论文

tech2024-12-06  7

· 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.

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