IJCAI'19最新推荐系统论文分享

IJCAI'19最新推荐系统论文分享_第1张图片

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.

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