一年一度的AI盛会IJCAI将于2019年8月10日至16日在中国澳门举行，在此特整理关于推荐系统方向最新的论文列表，希望对大家有所帮助。通过整理论文列表发现：
① 深度学习技术应用于推荐系统领域依然保持火热的势头。其中笔者尝试通过搜索[deep]关键字，结果找到了92个相关项，可见深度学习作品星罗棋布。
② 关于推荐系统领域的研究多点开花，研究方向涉及社会化推荐、视频推荐、可解释性推荐、序列化/会话推荐、POI推荐以及异构信息网络上的推荐、跨域推荐等。
③ 推荐系统领域知名学者依然保持高产。其中微软亚研院的谢幸老师6篇，新加坡国立大学的何向南老师5篇，东北大学的郭贵冰老师2篇。另外，Irwin King，Jiliang Tang等大佬也有论文入选。总之希望有越来越多的推荐系统大佬能够出现在此行列。
社会化推荐
- Wenqi et al. Deep Adversarial Social Recommendation.
- Guibing et al. Discrete Trust-aware Matrix Factorization for Fast Recommendation.
- Federico et al. Recommending Links to Maximize the Influence in Social Networks.
- Qitian Wu et al. Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust.
- Yongji et al. Graph Convolutional Networks on User Mobility Heterogeneous Graphs for Social Relationship Inference.
深度学习推荐
- Zeping et al. Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation.
- Dong Xi et al. BPAM: Recommendation Based on BP Neural Network with Attention Mechanism.
- Xin et al. CFM: Convolutional Factorization Machines for Context-Aware Recommendation.
- Xiao Zhou et al. Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems.
- Junyang et al. Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty.
- Liang et al. Matching User with Item Set: Collaborative Bundle Recommendation with Attention Network.
- Chuhan et al. Neural News Recommendation with Attentive Multi-View Learning.
- Qiong et al. PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation.
- Jiani et al. STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems.
可解释性推荐
- Zhongxia et al. Co-Attentive Multi-Task Learning for Explainable Recommendation.
- Min et al. Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach.
序列/会话推荐
- Guibing et al. Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation.
- Tingting et al. Feature-level Deeper Self-Attention Network for Sequential Recommendation.
- Chengfeng et al. Graph Contextualized Self-Attention Network for Session-based Recommendation.
- Yejin et al. Sequential and Diverse Recommendation with Long Tail.
- Jing Song et al. ISLF: Interest Shift and Latent Factors Combination Model for Session-based Recommendation.
- Shoujin et al. Sequential Recommender Systems: Challenges, Progress and Prospects.
- Chenliang et al. A Review-Driven Neural Model for Sequential Recommendation.
视频推荐
- Huan et al. DeepAPF: Deep Attentive Probabilistic Factorization for Multi-site Video Recommendation.
- Shengze et al. Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation.
- Jia et al. Multi-View Active Learning for Video Recommendation.
异质信息网络推荐
- Yanan et al. Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation.
- Zekai et al. Unified Embedding Model over Heterogeneous Information Network for Personalized Recommendation.
跨域推荐
- Feng et al. DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns.
强化学习推荐
- Eugene et al. SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets.
