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