IJCAI'19最新推荐系统论文分享

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.