Recommender Systems

DAN: Deep Attention Neural Network for News Recommendation AAAI 2019 DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System AAAI 2019 Deep Interest Evolution Network for Click-Through Rate Prediction AAAI 2019 Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems AAAI 2019 DeepRec: An Open-source Toolkit for Deep Learning based Recommendation IJCAI 2019 Deep Session Interest Network for Click-Through Rate Prediction IJCAI 2019 Discrete Social Recommendation AAAI 2019 Discrete Trust-aware Matrix Factorization for Fast Recommendation IJCAI 2019 DMRAN:A Hierarchical Fine-Grained Attention-Based Network for Recommendation IJCAI 2019 Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network EMNLP 2019 Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random ICML 2019 DSINE: Deep Structural Influence Learning via Network Embedding AAAI 2019 Dynamic Explainable Recommendation Based on Neural Attentive Models AAAI 2019 Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation IJCAI 2019 Evaluating Recommender System Stability with Influence-Guided Fuzzing AAAI 2019 Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach IJCAI 2019 Explainable Recommendation through Attentive Multi-View Learning AAAI 2019 Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust IJCAI 2019 Feature-level Deeper Self-Attention Network for Sequential Recommendation IJCAI 2019 From Implicit to Explicit Feedback: A deep neural network for modeling the sequential behavior of online users ACML 2019 From Recommendation Systems to Facility Location Games AAAI 2019 GeoStyle: Discovering Fashion Trends and Events ICCV 2019 HARE: a Flexible Highlighting Annotator for Ranking and Exploration EMNLP 2019 Hashtag Recommendation for Photo Sharing Services AAAI 2019 HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation AAAI 2019